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Record W2178065441 · doi:10.25071/1916-4467.40246

Maker pedagogy and science teacher education

2015· article· en· W2178065441 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2015
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCurriculumMovement (music)SociologyValue (mathematics)Variety (cybernetics)Function (biology)HackerProcess (computing)The artsPedagogyComputer scienceAestheticsVisual arts

Abstract

fetched live from OpenAlex

Making is a process that people engage in to design, create, and develop things that are of value and use to them personally or for their community. The recent popular (and sometimes commercial) Maker Movement is rooted in making and traces its lineage from a variety of historical precedents, including ancient traditions of arts and crafts fairs, tinkering and inventing using analog technologies, and hacking and programing with digital technologies. So-called “Maker Spaces” often function as co-ops that allow people to come together to build things, share expensive tools, and learn skills from one another. In this article, we will use the maker movement as a catalyst to reveal both some perennial challenges of and potential ways forward for curriculum studies of science and technology teacher education. In particular, we suggest that maker pedagogy, an approach to working with teacher candidates drawing from principles in the maker movement represents a potentially useful way forward in engaging teacher candidates in thinking about curriculum and working with students.

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)0.003
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.341
Teacher spread0.308 · 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