MétaCan
Menu
Back to cohort
Record W4212770207 · doi:10.1080/2331186x.2022.2034239

“Forget about the learning”? Technology expertise and creativity as experiential habit in hacker-/makerspaces

2022· article· en· W4212770207 on OpenAlex

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

VenueCogent Education · 2022
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsUniversity of Windsor
FundersEuropean Commission
KeywordsCreativityExperiential learningPsychologyHackerEducational technologyHabitQualitative researchPedagogySocial psychologySociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

This paper discusses to what extent hacker- and makerspaces (HMS) facilitate technology expertise. It draws on a combined qualitative interview and survey study of current/former community members. Study participants relate that HMS encourage learning-by-doing and self-directed creativity involving digital technology and crafts. Despite some being hesitant to label what they do as learning, a notion strongly associated with primary/secondary school, creativity itself is considered a learning ability and an experiential habit: a skill to be nurtured in practice. Members tend to expect that a self-directed approach to technological creativity is cultivated by new members too. As a “rite of passage”, this has implications for members’ in- and exclusion: notably creating challenges for individuals from already underrepresented groups and those perceiving themselves as comparatively low-skilled in technology. While learning and technology expertise are thus potentially facilitated in HMS, this is not equally the case for all members.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.024
GPT teacher head0.369
Teacher spread0.345 · 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