# 06 — Information Architecture **The Curiosity Lab** Version: 0.1 Status: Working Draft Path: `/research/domains/06-Information-Architecture.md` --- ## 1. Purpose This document defines the sixth research domain for The Curiosity Lab: **information architecture**. Its purpose is to establish how information should be organised, labelled, connected and experienced so that users can move from encounter to curiosity, from curiosity to connection, and from connection to understanding. This paper bridges: ```text research domains ↓ knowledge representation ↓ ontology ↓ experience patterns ↓ MVP design ``` The central concern is not simply whether information can be found. The central concern is whether information environments can help users: ```text notice orient ask choose traverse connect understand continue ``` This document asks: > **What kind of information architecture supports curiosity-oriented exploration without overwhelming, manipulating or disorienting the user?** --- ## 2. Current Status This is a Version 0.1 working draft. It is suitable for: - defining IA principles for The Curiosity Lab; - identifying candidate experience patterns; - informing `/architecture/Ontology.md`; - informing `/architecture/Experience-Patterns.md`; - informing the MVP backlog; - preparing for `/research/domains/07-Patterns-Across-Disciplines.md`. It is not yet suitable for: - final IA specification; - production UX design; - validated navigation patterns; - full interaction model; - claims about user outcomes. Current confidence: **Moderate** Reason: - Information architecture, exploratory search, sensemaking and information foraging provide relevant evidence. - The previous domain papers have created clear IA requirements. - The exact curiosity-oriented IA model remains unvalidated. - There is a risk of over-linking, over-mapping and over-designing. - MVP testing is required. --- ## 3. Domain Question The domain question is: > **How should information be structured so that users can follow meaningful curiosity journeys?** Supporting questions: 1. How does IA differ from ontology and knowledge representation? 2. How does curiosity-oriented IA differ from conventional findability? 3. How should users enter a knowledge environment? 4. How should users know where they are? 5. How should users choose where to go next? 6. How should connections be labelled? 7. How much structure should be visible? 8. When should the system reveal complexity? 9. How should search, browsing and guided journeys interact? 10. How can IA support sensemaking rather than only navigation? --- ## 4. Working Definitions --- ### 4.1 Information Architecture Information architecture is the organisation, structuring and labelling of information environments so that people can find, understand and use information. For The Curiosity Lab, this definition is extended: > **Information architecture is the design of meaningful pathways through knowledge.** This means IA is not only about locating information. It is also about helping the user understand why one thing leads to another. --- ### 4.2 Organisation Organisation concerns how information is grouped, ordered and structured. Examples: ```text topic hierarchy timeline map network taxonomy journey collection theme pattern ``` For The Curiosity Lab, organisation must support both clarity and discovery. --- ### 4.3 Labelling Labelling concerns the words used to describe categories, relationships, actions and pathways. Examples: ```text Related Caused by Contrasts with Reveals Opens a question about Similar pattern ``` Labels are not cosmetic. Labels shape meaning. --- ### 4.4 Navigation Navigation concerns how users move through an information environment. Examples: ```text menu breadcrumb link map card timeline next step return path ``` For The Curiosity Lab, navigation must support exploration without producing disorientation. --- ### 4.5 Search Search is a way of expressing an information need. However, curiosity may begin before the user has a precise query. The system must support both: ```text direct search exploratory search guided discovery question-led navigation ``` --- ### 4.6 Path A path is a sequence of information encounters. A path may be: ```text linear branching guided user-generated question-led object-led narrative exploratory ``` In The Curiosity Lab, paths should help users move toward meaningful connection. --- ### 4.7 Doorway A doorway is a labelled transition from one information element to another. It is more than a link. A doorway explains why the next step matters. Example: ```text This coin opens a doorway into imperial authority because its imagery carried power across distance. ``` --- ### 4.8 Information Scent Information scent is the set of cues that helps users estimate whether a path is worth following. For The Curiosity Lab, scent may come from: ```text label preview question image relationship type source cue confidence cue expected payoff ``` --- ### 4.9 Sensemaking Sensemaking is the process of gathering, organising, restructuring and interpreting information into a coherent understanding. The Curiosity Lab should support sensemaking, not just movement. --- ## 5. Core Distinctions --- ### 5.1 Information Architecture vs Knowledge Representation Knowledge representation defines what the system can represent internally. Information architecture defines how the user experiences and navigates those structures. ```text Knowledge Representation: Object raises_question Question. Information Architecture: A user sees a card asking: "What question does this object open?" ``` Design implication: ```text The ontology should support IA, but IA should not simply expose the ontology. ``` --- ### 5.2 Information Architecture vs User Interface The user interface is the visible interaction surface. Information architecture is the underlying structure that makes navigation and meaning possible. A beautiful interface can still have weak IA. A plain interface can still have strong IA. Design implication: ```text First design the structure of meaning, then design the surface. ``` --- ### 5.3 Findability vs Curiosity Conventional IA often optimises findability. The Curiosity Lab also needs findability, but it is not the final goal. A user may not yet know what they want to find. Curiosity-oriented IA should help users discover what is worth asking. Design implication: ```text Support finding, but also support question formation. ``` --- ### 5.4 Link vs Relationship A link connects two pieces of content. A relationship explains why they are connected. Example: ```text Weak: Related article Stronger: This connects to trade because coin distribution reveals movement across the empire. ``` Design implication: ```text Every important link should be treated as a relationship with meaning. ``` --- ### 5.5 Browsing vs Exploration Browsing may be casual movement. Exploration involves evolving interest, question formation and sensemaking. The Curiosity Lab should not equate page-to-page movement with meaningful exploration. Design implication: ```text Measure whether movement changes questions, explanations or connections. ``` --- ### 5.6 More Choices vs Better Choices More links may create more freedom. They may also create overload. Curiosity-oriented IA should present a manageable set of meaningful options. Design implication: ```text Curate high-value doorways rather than expose every possible connection. ``` --- ### 5.7 Map vs Journey A map shows possible structure. A journey provides direction through structure. Both may be useful. Neither should replace the other. Design implication: ```text Use maps for orientation and journeys for progression. ``` --- ## 6. Major Theoretical Strands --- ## 6.1 Information Architecture as Organisation, Labelling, Navigation and Search Rosenfeld, Morville and Arango describe IA in terms of core systems such as organisation, labelling, navigation and search. This is directly useful because The Curiosity Lab needs all four. However, the programme’s distinctive contribution is to apply those systems to curiosity and meaning. For The Curiosity Lab: ```text organisation supports coherent knowledge structures labelling explains why connections matter navigation supports curiosity journeys search supports direct and exploratory inquiry ``` ### Implication for The Curiosity Lab The platform should not rely only on generated content. It needs designed structures. ### Design principle > **Curiosity requires organised pathways, meaningful labels and recoverable navigation.** ### Architecture implication Potential IA structures: ```text OrganisationSystem LabellingSystem NavigationSystem SearchSystem RelationshipLabel JourneyStructure ``` ### MVP implication Define relationship labels and doorway patterns before building a large content corpus. ### Confidence Moderate. The IA framework is highly relevant, though practitioner-oriented. --- ## 6.2 Exploratory Search: From Finding to Understanding Marchionini distinguishes exploratory search from simple lookup. Exploratory search is relevant when users are learning, investigating or forming an understanding. This fits The Curiosity Lab because users may begin with: ```text a vague interest a surprising object an image a question a half-known topic ``` They may not know the right query yet. ### Implication for The Curiosity Lab The system should support evolving information needs. Users should be able to move between: ```text search browse question guided path map reflection ``` ### Design principle > **Curiosity-oriented IA should support users whose questions are still forming.** ### Architecture implication Potential entities: ```text ExploratoryPath EvolvingQuestion Waypoint Branch ReturnPath SearchSession ``` Potential relationships: ```text refines_question branches_to returns_to supports_understanding ``` ### MVP implication Compare a direct answer page with a guided exploratory path that helps users form a better question. ### Confidence High for exploratory search relevance. Moderate for exact MVP implementation. --- ## 6.3 Berrypicking and Evolving Information Needs Bates’s berrypicking model is important because it challenges the idea that information seeking is a single query followed by retrieval. Instead, users often gather information bit by bit. Their questions evolve as they encounter new material. This is highly relevant to curiosity journeys. A user might begin with: ```text What is this coin? ``` then move to: ```text Why is the emperor shown? How did coins travel? What did people trust? How did empire communicate? Where else does this pattern appear? ``` The question changes because the user learns. ### Implication for The Curiosity Lab The system should preserve and support evolving trails. A curiosity journey is not failure because it changes direction. It may be successful precisely because the user’s question improved. ### Design principle > **A curiosity journey should allow the user’s question to evolve.** ### Architecture implication Potential entities: ```text QuestionTrail QueryShift Encounter CollectedFragment EvolvingInterest ``` Potential relationships: ```text evolves_into collected_from changes_direction_to ``` ### MVP implication Track whether users refine or deepen their questions during a journey. ### Confidence Moderate to High. Strong relevance, but the first MVP should keep this simple. --- ## 6.4 Information Foraging and Information Scent Pirolli and Card’s information foraging theory treats information seeking partly through the lens of cost, value and cues. Users follow signals that suggest where useful information may be found. This is relevant because curiosity-oriented IA must signal the value of possible next steps. A doorway needs scent. Weak scent: ```text More Related Next ``` Stronger scent: ```text Why this coin mattered politically How trade moved objects across the empire A modern parallel: passports and trust ``` ### Implication for The Curiosity Lab Doorways should communicate expected value. Users should know why a path is worth taking. ### Design principle > **A doorway should signal the curiosity value of the next step.** ### Architecture implication Potential attributes: ```text information_scent expected_value effort_cost complexity_level doorway_label preview ``` ### MVP implication Compare generic links with labelled doorways that make value explicit. ### Confidence High for information scent relevance. Moderate for scoring scent in the MVP. --- ## 6.5 Sensemaking and the Cost of Coherence Russell, Stefik, Pirolli and Card describe sensemaking as involving costs of gathering, representing and restructuring information. This matters because curiosity journeys generate fragments. The system should help users turn fragments into coherence. A curiosity path might include: ```text object date symbol place event trade route political system modern analogy ``` Without support, this becomes clutter. With sensemaking support, it becomes understanding. ### Implication for The Curiosity Lab The system needs sensemaking aids: ```text trail summary map segment relationship labels reflection prompt return-to-object step ``` ### Design principle > **Curiosity journeys should reduce the cost of turning fragments into coherence.** ### Architecture implication Potential entities: ```text SensemakingTrail Fragment Summary MapSegment Reflection Reframe ``` Potential relationships: ```text organises summarises integrates supports_reflection ``` ### MVP implication Test whether a visible journey trail improves explanation quality and orientation. ### Confidence High for sensemaking relevance. Moderate for exact interface pattern. --- ## 6.6 Progressive Disclosure and Cognitive Load Cognitive load theory cautions that too much complexity can impair learning. This is critical for The Curiosity Lab because the project naturally tends toward graphs, connections and cross-domain patterns. A connected knowledge system can become overwhelming. The problem is not only: ```text Can the system show connections? ``` The better question is: ```text When should each connection be revealed? ``` ### Implication for The Curiosity Lab The IA should reveal complexity progressively. Do not show the whole graph too early. Present a small number of meaningful doorways. Allow deeper structure to unfold as the user chooses. ### Design principle > **Reveal complexity only when it helps the user continue or understand.** ### Architecture implication Potential entities: ```text ProgressiveDisclosureStep ComplexityLevel ChoiceSet Scaffold CognitiveLoadSignal ``` Potential relationships: ```text reveals_progressively limits_choice reduces_load scaffolds ``` ### MVP implication Compare full graph exposure with curated progressive doorways. ### Confidence High for load constraint. Moderate for curiosity-specific balance. --- ## 6.7 Wayfinding and Orientation Information environments require orientation. Users need to know: ```text Where am I? How did I get here? Why am I seeing this? Where can I go next? Can I return? What have I learned? ``` Curiosity does not eliminate the need for orientation. In fact, exploratory environments may require stronger orientation support because paths are less predictable. ### Implication for The Curiosity Lab The platform should include orientation structures: ```text breadcrumb journey trail current question current object current frame map segment return path ``` ### Design principle > **Exploration requires orientation; curiosity should not feel like being lost.** ### Architecture implication Potential entities: ```text LocationInJourney Breadcrumb CurrentQuestion CurrentFrame ReturnPoint JourneyHistory ``` ### MVP implication Test whether journey trails improve user confidence and continuation. ### Confidence Moderate. Strong IA principle; needs MVP validation. --- ## 6.8 Relationship Labelling The previous domain papers repeatedly point to relationship labels as a core IA element. A relationship label is not only a technical property. It is the user’s explanation of why a connection matters. Example labels: ```text caused_by led_to reveals contrasts_with is_analogous_to modern_parallel raises_question_about is_evidence_for ``` In user-facing form, these may become natural language: ```text Reveals how power travelled Shows a similar pattern Raises a question about trust Contrasts with modern borders ``` ### Implication for The Curiosity Lab Relationship labels should be designed, tested and governed. They are central to the IA. ### Design principle > **Meaningful relationship labels are the grammar of curiosity-oriented IA.** ### Architecture implication Potential structures: ```text RelationshipType SystemLabel UserLabel Explanation Evidence Confidence Example ``` ### MVP implication Test multiple label styles for the same underlying relationship. ### Confidence Moderate to High. The pattern is strongly supported across previous papers, but exact taxonomy remains open. --- ## 6.9 Guided Paths vs Open Graphs The Curiosity Lab is likely to use graph structures internally. However, user-facing experience should not default to an open graph. Open graphs may be useful for expert users, researchers or advanced exploration. Novice users may benefit more from curated paths. A useful distinction: ```text Graph: the field of possible meaningful connections Path: a guided route through selected connections Doorway: a labelled invitation to move from one point to another Map: a visible orientation aid ``` ### Implication for The Curiosity Lab The system should design paths through the graph rather than simply expose the graph. ### Design principle > **The graph supports the system; the path supports the user.** ### Architecture implication Potential entities: ```text Graph Path Doorway MapSegment Waypoint ChoicePoint ``` ### MVP implication Compare graph-like browsing with curated doorway-based journeys. ### Confidence Moderate. Strong design logic; requires testing. --- ## 6.10 Search, Browse and Guide as Complementary Modes The platform should not choose between search, browsing and guided journeys. Each mode supports a different user state. ```text Search: I know what I want. Browse: I am open to discovery. Guide: Help me understand why this matters. ``` A curiosity-oriented system should allow movement between these modes. A user might start with an object, browse connections, ask a question, then enter a guided path. ### Implication for The Curiosity Lab The IA should support mode transitions. ### Design principle > **Curiosity-oriented IA should let users move between search, browsing and guided exploration.** ### Architecture implication Potential entities: ```text Mode SearchMode BrowseMode GuideMode ModeTransition ``` Potential relationships: ```text transitions_to supports initiates ``` ### MVP implication Start with guided journeys and simple doorway browsing; defer full search until content volume justifies it. ### Confidence Moderate. --- ## 7. Working Model: Curiosity-Oriented IA The current working model is: ```text Entry Point ↓ Orientation ↓ Curiosity Prompt ↓ Doorway Choice ↓ Meaningful Connection ↓ Sensemaking Support ↓ Reflection ↓ Continuation / Return ``` This model says that curiosity-oriented IA must solve seven practical problems: ```text How does the user enter? How do they know where they are? What makes them curious? How do they choose where to go? Why does the connection matter? How do they integrate what they find? What invites continuation? ``` --- ## 8. Relationship to the Curiosity Cycle The Curiosity Cycle is: ```text Observation ↓ Curiosity ↓ Connection ↓ Understanding ↓ Wonder ↓ Renewed Observation ``` Information architecture supports the transitions. | Cycle Stage | IA Requirement | |---|---| | Observation | entry point, guided noticing, object framing | | Curiosity | prompt, gap, question, tension | | Connection | labelled doorway, relationship explanation | | Understanding | trail, summary, map, reflection | | Wonder | open question, next doorway, unresolved remainder | | Renewed Observation | return path, changed frame, new entry point | --- ## 9. Design Principles The following principles are provisional. --- ### IA-001 — Design pathways, not just pages The unit of experience should be the journey, not the isolated content item. --- ### IA-002 — Use doorways instead of generic links A doorway should explain why the user might move from one item to another. --- ### IA-003 — Make relationship labels meaningful Labels should communicate relationship type and significance. --- ### IA-004 — Support evolving questions Users should be able to refine, deepen or change their questions as they learn. --- ### IA-005 — Reveal complexity progressively Do not expose all possible connections at once. --- ### IA-006 — Preserve orientation Users should know where they are, how they arrived and where they can return. --- ### IA-007 — Support sensemaking The architecture should help users integrate fragments into coherent understanding. --- ### IA-008 — Balance guidance and agency The system should guide without forcing a single route. --- ### IA-009 — Separate internal graph from user-facing experience The graph may power the experience, but the user should see meaningful paths, not raw structure. --- ### IA-010 — Measure understanding, not only movement Clicks and path length are insufficient. The system should measure whether users form better questions, explanations and connections. --- ## 10. Architecture Implications Information architecture suggests the following structures for `/architecture/Ontology.md` and `/architecture/Experience-Patterns.md`. --- ### 10.1 Candidate Entities ```text EntryPoint Orientation CuriosityPrompt Doorway Path Waypoint ChoicePoint RelationshipLabel InformationScent ExploratoryPath QuestionTrail SensemakingTrail MapSegment Breadcrumb ReturnPath Reflection ContinuationPrompt Mode ``` --- ### 10.2 Candidate Relationships ```text starts_at orients_to raises_question opens_doorway_to branches_to returns_to labels_relationship signals_value refines_question supports_sensemaking summarises invites_reflection continues_to ``` --- ### 10.3 Candidate Attributes ```text entry_type doorway_strength label_type information_scent expected_value complexity_level prior_knowledge_required orientation_level choice_count confidence_level evidence_category user_facing_label system_relationship_type ``` --- ## 11. MVP Implications The Information Architecture domain suggests several MVP experiments. --- ### EXP-IA-001 — Generic Link vs Doorway Research question: > Does a meaningful doorway label increase curiosity and understanding compared with a generic link? Variants: ```text A: Related B: Reveals how power travelled C: Opens a question about trust ``` Measures: - click-through; - relationship recall; - explanation quality; - self-reported curiosity; - trust. --- ### EXP-IA-002 — Curated Doorways vs Full Graph Research question: > Does a small curated set of doorways outperform a larger visible graph? Variants: ```text A: many visible connections B: three curated doorways ``` Measures: - continuation; - perceived overload; - choice confidence; - explanation quality; - relationship recall. --- ### EXP-IA-003 — Journey Trail Research question: > Does showing users their path improve orientation and understanding? Variants: ```text A: no trail B: visible journey trail ``` Measures: - orientation confidence; - ability to summarise journey; - return behaviour; - continuation; - cognitive load. --- ### EXP-IA-004 — Question-Led Entry vs Topic-Led Entry Research question: > Does beginning with a question increase curiosity compared with beginning with a topic label? Variants: ```text A: Roman coinage B: How did a coin carry power across an empire? ``` Measures: - continuation; - question generation; - recall; - explanation quality; - perceived purpose. --- ### EXP-IA-005 — Progressive Disclosure Research question: > Does revealing connections progressively improve understanding compared with showing many options at once? Variants: ```text A: all connections visible B: staged reveal of connections ``` Measures: - overload; - continuation; - relationship recall; - explanation quality. --- ### EXP-IA-006 — Sensemaking Summary Research question: > Does a short summary after several nodes improve understanding? Variants: ```text A: no summary B: generated / authored sensemaking summary ``` Measures: - explanation coherence; - confidence; - recall; - continuation; - reflection quality. --- ## 12. Measurement Candidates Information architecture should be evaluated by whether it supports meaningful movement. Possible behavioural indicators: ```text chooses a doorway continues after explanation returns to previous object refines a question opens map / trail follows labelled relationship chooses deeper path submits reflection ``` Possible meaning indicators: ```text can explain why two things are connected can recall relationship label can summarise journey can identify current question can ask a better follow-up question can transfer pattern to another case ``` Possible negative indicators: ```text rapid backtracking choice paralysis low confidence high confusion generic clicking failure to explain connection ``` Possible self-report prompts: ```text I understood why this connection was offered. I knew where I was in the journey. I wanted to follow another doorway. I felt overwhelmed by the number of options. This helped me ask a better question. ``` --- ## 13. Implications for The Curiosity Lab Hypothesis Current hypothesis: > **Meaningful curiosity emerges when knowledge is experienced as an interconnected network rather than as isolated information.** Information architecture refines this hypothesis. A better version is: > **Meaningful curiosity is more likely when users encounter a structured sequence of labelled, intelligible and choice-sensitive connections that support orientation, sensemaking and continuation.** This matters because it avoids the weak assumption: ```text more connections = more curiosity ``` The stronger claim is: ```text better structured connections may support curiosity. ``` Current confidence: **Moderate** --- ## 14. Known Weaknesses This document has the following weaknesses: 1. It relies heavily on IA, HCI and information-seeking literature but has not yet tested curiosity-specific IA. 2. It does not yet define a final relationship label taxonomy. 3. It does not yet distinguish all user types. 4. It assumes users want exploratory journeys some of the time, but many may prefer direct answers. 5. It may underrepresent accessibility and inclusive design. 6. It does not yet include enough research on recommender systems. 7. It does not yet include enough sources on digital wayfinding. 8. It does not yet define the visual design of maps or trails. 9. It risks over-structuring curiosity. 10. No MVP validation exists. --- ## 15. Contradictions and Cautions --- ### 15.1 Curiosity is not always served by exploration Sometimes the right answer is a direct answer. The platform should not force exploration when the user wants retrieval. --- ### 15.2 More links can reduce meaning Excessive connections may create overload. --- ### 15.3 Labels can mislead A relationship label can overstate causality, certainty or relevance. Labels need governance. --- ### 15.4 Guided paths can become controlling Guidance supports sensemaking, but too much guidance can reduce agency. --- ### 15.5 Maps can overwhelm A map may orient some users and confuse others. --- ### 15.6 Search must not be neglected Exploration is important, but users still need ways to find specific things. --- ## 16. Research Debt | ID | Debt | Priority | Status | |---|---|---:|---| | RD-IA-001 | Register Marchionini as a full Research Register entry. | High | Open | | RD-IA-002 | Register Bates berrypicking model. | High | Open | | RD-IA-003 | Register Pirolli & Card information foraging. | High | Open | | RD-IA-004 | Register Russell et al. sensemaking. | High | Open | | RD-IA-005 | Register Rosenfeld, Morville & Arango. | Medium | Open | | RD-IA-006 | Add sources on wayfinding in digital environments. | Medium | Open | | RD-IA-007 | Add accessibility and inclusive design sources. | High | Open | | RD-IA-008 | Add sources on recommender systems and serendipity. | Medium | Open | | RD-IA-009 | Define relationship label taxonomy. | High | Open | | RD-IA-010 | Define doorway scoring criteria. | High | Open | | RD-IA-011 | Define orientation patterns. | Medium | Open | | RD-IA-012 | Test generic links vs doorways. | High | Open | | RD-IA-013 | Test curated doorways vs full graph. | High | Open | | RD-IA-014 | Translate candidate entities into Ontology.md. | High | Open | | RD-IA-015 | Translate patterns into Experience-Patterns.md. | High | Open | --- ## 17. Register Entries to Create or Expand Minimum entries: ```text RR-IA-0001 — Marchionini (2006), Exploratory Search RR-IA-0002 — Bates (1989), Berrypicking Model RR-IA-0003 — Pirolli & Card (1999), Information Foraging RR-IA-0004 — Russell, Stefik, Pirolli & Card (1993), Sensemaking RR-IA-0005 — Rosenfeld, Morville & Arango (2015), Information Architecture RR-IA-0006 — Sweller (1988), Cognitive Load ``` Additional entries to consider: ```text Morville — Ambient Findability Spencer — Card Sorting / IA practice Nielsen Norman Group — information scent and UX practice Wilson — information behaviour Kuhlthau — information search process ``` --- ## 18. Immediate Architecture Promotions The following items should be considered for `/architecture/Ontology.md`. ### Candidate Entities ```text EntryPoint Doorway Path Waypoint ChoicePoint RelationshipLabel InformationScent QuestionTrail SensemakingTrail MapSegment Breadcrumb ReturnPath Reflection Mode ``` ### Candidate Relationships ```text starts_at opens_doorway_to branches_to returns_to labels_relationship signals_value refines_question supports_sensemaking invites_reflection continues_to ``` ### Candidate Attributes ```text doorway_strength label_type information_scent expected_value complexity_level orientation_level choice_count user_facing_label system_relationship_type ``` Status: **Provisional** --- ## 19. Immediate Pattern Promotions The following items should be considered for `/architecture/Pattern-Catalogue.md`. ```text Doorway Instead of Link Question-Led Entry Curated Doorways Progressive Disclosure Journey Trail Map Segment Return to Object Relationship Label Sensemaking Summary Search-Browse-Guide Modes ``` Status: **Emerging** --- ## 20. Immediate MVP Promotions The following items should be considered for `/mvp/Experiments.md`. ```text EXP-IA-001 — Generic Link vs Doorway EXP-IA-002 — Curated Doorways vs Full Graph EXP-IA-003 — Journey Trail EXP-IA-004 — Question-Led Entry vs Topic-Led Entry EXP-IA-005 — Progressive Disclosure EXP-IA-006 — Sensemaking Summary ``` Status: **Candidate** --- ## 21. Relationship to Previous Domain Papers --- ### Relationship to 01-Curiosity.md Information architecture turns curiosity concepts into traversable structures: ```text Knowledge Gap Question Doorway Continuation ``` --- ### Relationship to 02-Knowledge-and-Meaning.md Information architecture supports meaning through: ```text relationship labels sensemaking trails surface-to-structure movement transfer paths ``` --- ### Relationship to 03-Narrative.md Information architecture can use narrative as one form of guided path: ```text event tension cause consequence unresolved question ``` --- ### Relationship to 04-Museum-Interpretation.md Information architecture supports object-led interpretation through: ```text guided observation interpretive doorway context layering return to object ``` --- ### Relationship to 05-Knowledge-Representation.md Information architecture exposes selected parts of the internal representation through user-facing structures: ```text doorways labels paths maps prompts trails ``` --- ## 22. Provisional Conclusion Information architecture is the discipline that turns connected knowledge into navigable experience. The strongest conclusion of this document is: > **The Curiosity Lab should not simply connect information. It should design meaningful, labelled, progressively revealed pathways that help users orient, choose, connect and make sense.** The key warning is: > **More connections do not automatically create more curiosity.** A curiosity-oriented information architecture should help the user move through: ```text entry ↓ orientation ↓ question ↓ doorway ↓ connection ↓ sensemaking ↓ reflection ↓ continuation ``` Current confidence: **Moderate** --- ## 23. References Bates, M. J. (1989). The design of browsing and berrypicking techniques for the online search interface. *Online Review*, 13(5), 407–424. https://doi.org/10.1108/eb024320 Marchionini, G. (2006). Exploratory search: From finding to understanding. *Communications of the ACM*, 49(4), 41–46. https://doi.org/10.1145/1121949.1121979 Pirolli, P., & Card, S. K. (1999). Information foraging. *Psychological Review*, 106(4), 643–675. https://doi.org/10.1037/0033-295X.106.4.643 Rosenfeld, L., Morville, P., & Arango, J. (2015). *Information Architecture: For the Web and Beyond* (4th ed.). O’Reilly Media. Russell, D. M., Stefik, M. J., Pirolli, P., & Card, S. K. (1993). The cost structure of sensemaking. *Proceedings of INTERCHI '93*, 269–276. https://doi.org/10.1145/169059.169209 Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. *Cognitive Science*, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4 --- ## 24. Next Action Do not produce `07-Patterns-Across-Disciplines.md` immediately unless the programme intentionally accepts a synthesis pass. The stronger next artefacts are: ```text /architecture/Ontology.md /architecture/Experience-Patterns.md /architecture/Pattern-Catalogue.md ``` Recommended next file: ```text /architecture/Ontology.md ``` Reason: The first six domain papers have now generated enough candidate entities, relationships and attributes to justify a provisional ontology.