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Record W616250132

Challenges of Physics Education Research in Canada

2011· article· en· W616250132 on OpenAlexaboutno aff
Tetyana Antimirova

Bibliographic record

VenueScholarship@Western (Western University) · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsPhysics educationPolitical scienceMathematics educationEngineering ethicsSociologyPedagogyEngineeringPsychology
DOInot available

Abstract

fetched live from OpenAlex

One may ask why there are only few subject-based Science Education Research groups in Canada, unlike in USA, Europe, Australia or Latin America where subject-based Science Education Research flourishes. The main reason is the virtual absence of funding for subject-based Science Education Research at the national and provincial levels, with very few exceptions. In the absence of sustainable long-term funding, graduate programs based within Science Departments cannot be established. Another problem that hinders the development of the field is a disconnect that exists between Science Departments and Faculties of Education. Physics Education Research (PER) provides a good example for examining the challenges faced by subject-based Education Research in Canada. Almost all recent Canadian PER initiatives happened despite the lack of PER funding at both national and provincial levels. These efforts are initiated by the individuals, small groups and some universities. As a result, mostly a patchwork of short-term PER research projects currently exists in Canada, and the long-term sustainability of these research efforts remains problematic. As an outgoing Chair of the Division of Physics Education of the Canadian Association of Physicists, I will provide a few case studies of recent successful physics education initiatives in Canada. Despite the difficulties we face, PER movement in Canada is building slowly from the ground up. However, the long-term future of PER in Canada remains uncertain. The united effort by science educators representing various science disciplines is needed to overcome present challenges imposed by the current lack of funding for Science Education Research.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.044
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.737

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.065
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0090.015
Science and technology studies0.0350.015
Scholarly communication0.0270.008
Open science0.0080.014
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0220.003

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 head0.526
GPT teacher head0.458
Teacher spread0.068 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainMethods
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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