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Makerspaces

2016· book-chapter· en· W2567464165 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

VenueAdvances in educational technologies and instructional design book series · 2016
Typebook-chapter
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTransformative learningCurriculumProcess (computing)SociologyMathematics educationPedagogyComputer sciencePsychology

Abstract

fetched live from OpenAlex

The emergence of the makerspace movement offers tremendous potential to transform learning. Learning by making, while ancient in practice, has evolved due to the development and confluence of developments in computing, communications technologies, pedagogy, and library science. In particular, online networking has enabled learners to share and engage with ideas and materials in a uniquely 21st century fashion. The makerspace activity process (MAP) framework illustrates how makerspace activities—curating, relating, and creating—are intertwined through networking practices. Makerspaces are highly contingent and transformative; both the nature of the makerspace and the participants transform each other through interaction. For those educators who find it difficult to integrate within formal curricula and assessment practices, the MAP framework provides a guide for facilitating and assessing learner activity in educational makerspaces. The framework is useful for educators at all levels from kindergarten to post-secondary.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.599
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
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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