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Record W4361199503 · doi:10.46827/ejae.v8i1.4721

APPRENTISSAGE ET ENSEIGNEMENT EN SCIENCES PHYSIQUES / LEARNING AND TEACHING IN PHYSICAL SCIENCES

2023· article· fr· W4361199503 on OpenAlexaff
Charilaos Voutsinos

Bibliographic record

VenueEuropean Journal of Alternative Education Studies · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsHumanitiesContext (archaeology)PsychologyPolitical sciencePhilosophyGeography

Abstract

fetched live from OpenAlex

<p>Dans cet article, nous tentons de soulever les questions caractéristiques générales de l'apprentissage et de l'enseignement des sciences physiques. L'accent est mis, d'une part, sur le caractère de l'apprentissage qui doit avoir des caractéristiques généralisables, une perspective de développement et une certaine indépendance par rapport aux connaissances scolaires institutionnelles et, d'autre part, sur l'enseignement qui doit suivre de manière créative les engagements des programmes et la compréhension des concepts et phénomènes connexes. Dans un tel contexte général, les problèmes ouverts sont discutés par les deux parties, mettant en évidence les convergences et les différences.</p><p>In this article an attempt is made to raise the general characteristic questions of learning and teaching in Physical Sciences. The emphasis is on the one hand on the character of learning which must have generalisable features, a developmental perspective, and a certain independence from institutional school knowledge, and on the other hand on teaching which must creatively follow the commitments of programs and the understanding of related concepts and phenomena. In such a general context, open problems are discussed by both sides, highlighting convergences and differences.</p><p> </p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0029/a.php" alt="Hit counter" /></p>

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 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)0.019
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.001
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 head0.286
GPT teacher head0.550
Teacher spread0.264 · 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 teacher head, not a consensus.

Study designQualitative
Domainnot available
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
Published2023
Admission routes1
Has abstractyes

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