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Record W4414251238 · doi:10.1007/978-981-96-9029-9_3

Collaborative Maps of Curiosity

2025· book-chapter· en· W4414251238 on OpenAlex
Catherine Hamel

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience for sustainable societies · 2025
Typebook-chapter
Languageen
FieldPsychology
TopicPsychological and Educational Research Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCuriosityCLARITYExperiential learningProcess (computing)Scale (ratio)Space (punctuation)

Abstract

fetched live from OpenAlex

Abstract Collaborative Maps of Curiosity is an invitation to visualize a city to explore its space toward creating alternative experiences for people adapting and integrating in environments. The method has been applied in a range of contexts, including communities of resettled refugees, explorations of public safety after addiction recovery, and the interplay of visual and olfactory experiences with participants living with Alzheimer’s disease. The proposed approach to collaborative map-making is a three-layered, adaptable framework, with each layer corresponding to a different scale of observation explored in a different medium. Beginning with the personal and expanding to the communal, mapping becomes a form of expression. Oscillating between clarity and disorder, the most meaningful aspect of the process lies in the exchange between participants. Initial individual images in one layer respond to neighboring depictions in the next, gradually developing and merging into a final experiential map of the participants’ new city—one that invites them to engage with unfamiliar places and embrace new experiences.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.120
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.391
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it