Teaching critical approaches. A resource drawn from research to help teachers foster pupils’ ability to engage in critical approaches across different contexts
Bibliographic record
Abstract
Table of contents Introduction -- Teaching critical thinking : nuancing the self-evident. -- Why rely on scientific research when developing a resource for teaching critical thinking? -- How to best use this resource -- The two types of need this resource could meet -- How this resource is structured Definitions. Teaching critical approaches -- Critical thinking or critical approaches? -- What are the characteristics of critical approaches? -- Diverse ambitions for critical approaches -- How do I discuss it with my pupils? Relationships to knowledge. Interrogating teacher and pupil stances -- Nature of Science* -- Croyances épistémiques* Metacognition and reflexivity. Acting on one’s thoughts -- Cognitive biases -- Metacognition -- A metacognition-related disposition to develop: intellectual humility -- Reflexivity, a complementary concept to metacognition Argumentation : provide reasons to justify a point of view within a validity domain -- Contenu de l’argument (inspiré de Pallares, 2019) -- Function of the argument relative to another argument (based on Pallares, 2019) -- Norms of argumentation -- Critical integrative argumentation*: a promising form of collaborative argumentation -- Dialogic argumentation: an approach focused on dialogue between pupils -- A whole class approach for all ages: philosophy for children and teenagers -- The role of emotion in argumentation 5 Searching for and evaluating information. Beyond media literacy -- Fake news: a real problem or alarmist discourse? -- Rethinking misinformation in light of research -- A cognitive view of finding information -- Limits of the cognitive approach and young people’s actual practices -- Making pupils want to search for information -- Models of information evaluation -- Pedagogical possibilities -- Angles and choices of media literacy themes Conclusion: fostering engagement in critical approaches across different contexts -- A conceptual problem -- An assessment problem -- A collective problem -- Global view: bringing together the different parts of this resource -- The limits of this resource and additional elements -- Final words References -- Introduction -- Definitions. Teaching critical approaches -- Relationships to knowledge. Interrogating teacher and pupil stances -- Metacognition and reflexivity. Acting on one’s thoughts -- Argumentation : provide reasons to justify a point of view within a validity domain -- Searching for and evaluating information. Beyond media literacy -- Conclusion Glossary
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.066 | 0.027 |
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".