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Record W3036723923 · doi:10.1111/jcal.12452

Bridging distance: Practical and pedagogical implications of virtual Makerspaces

2020· article· en· W3036723923 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

VenueJournal of Computer Assisted Learning · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAffordanceBridging (networking)Qualitative researchWork (physics)SociologyPsychologyComputer scienceKnowledge managementEngineeringHuman–computer interactionSocial science

Abstract

fetched live from OpenAlex

Abstract Makerspaces are locations where people with common interests can work on projects, share ideas, tools and expertise to make or create. There is an abundance of “how to” guides and research studies on physical makerspaces, little research focuses on describing the virtual making processes and the experiences therein. This qualitative study explores the experiences of seven participants who engaged in a synchronous virtual makerspace. Meeting once a month over 16 weeks, members of the International Maker Educator Network participated in the making of a robot. This case study describes how the virtual making occurred, the personal experiences of the makers, technology used to support virtual making, and the affordances and inhibitors of virtual making. Data are analysed through the lens of a professional learning community and the People, Means and Activities makerspace framework. The paper concludes with implications for virtual making in practice and future research opportunities.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.699
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.229
GPT teacher head0.467
Teacher spread0.238 · 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