A theoretical tool for research in science education
Bibliographic record
Abstract
This article pursues a dual objective. First, it seeks to present the notion of the relationship to knowledge as a valuable theoretical tool for science education research. Secondly, it aims to illustrate how this notion has been operationalized in recent research conducted in Que-bec (Canada) that focuses on teachers ‟ and students ‟ relationship to knowledge. The first portion of this article presents the notion of the relationship to knowledge, documenting its origins, usefulness and contributions to research in the field of science education. In the second portion, we present four (4) studies recently conducted in Quebec that relied on the notion of the relationship to knowledge to analyze, respectively: 1) postsecondary science students ‟ relationships to experts; 2) secondary students ‟ epistemological postures and rela-tionship to scientific knowledge; 3) the relationship to knowledge and school of primary and secondary students who repeated a school year; and 4) the relationships to knowledges (in the plural form) of preservice secondary science and social studies teachers. We also present one (1) project, in progress at this time, which is dedicated to the point of view of preservice primary teachers concerning science and science education. By way of conclu-sion, we set out some main avenues for further research and debate in science education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".