Bibliographic record
Abstract
For fifteen years, educational actors (teachers, school librarians, academic advisors ...), among which the teacher librarian plays a major part in the Centers for Documentation and Information (CDI, school library ), have been teaching, evaluating, requiring students expectations on a supposed "cultural background" , commonly called “general culture” in French, supported by the educational institution and the social world. In a first part, we wish to define the question of an overall school culture and show how the school librarians have slowly claimed the development and support for a "Culture of information " for each student and, more broadly, each citizen. In a second step, we discuss the similarities, differences and links between “basic knowledge” and information culture. In which way are they similar, fundamentally different, what is specific and new in the “culture of information” for young people? In a third step, we frame the components of the culture of information, deeply rooted into culture and the digital developments of information. The culture of information is the field of curricula, educative actions and debates, revealing tensions within libraries, between school requirements and social expectations. In a fourth and final step, we will show and advocate for a dynamic conception of academic culture of information "in action", connected to the media and social events, in classrooms and school libraries, updating the weak links between general education and information culture.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
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".