Place et rôle des langues et cultures de l'Antiquité dans l'enseignement du français à l'école primaire de 1882 à nos jours en France
Bibliographic record
Abstract
French language was and still remains the priority of French primary school. Despite this will, reality is a bit different: learning French remains complicated at school, there are big gaps between pupils. According to the results from PISA's survey (2012), our system of education is « very good » at creating even more inequalities between pupils. How can such difference be explained? Moreover, why is French so hard to teach and to learn? In order to answer these questions, a diachronic approach was salutary in many respects. At first, it is important to describe and to understand how our school was built. In addition, the place of Classics – Latin and Ancient Greek – was once great in our system, and their nearly disappearance had serious consequences on French learning. Indeed, the history of the language and the discipline shows us that French could not have been taught without classics for a long time. In our analysis, we question these links and we consider introducing classics in French learning at primary school. Classics reveal themselves to be very precious tools in order to create postures among pupils – reflexive, metalinguistic and epistemic – which allow them to succeed. They help all pupils to acquire strategies, which enable them to learn a rich language, therefore to develop a rich mind. Moreover, antic culture is at the basis of all fields of knowledge, it is a link between all disciplines and it makes them meaningful.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".