Interdisciplinarity in Primary and Secondary School: Issues and Perspectives
Bibliographic record
Abstract
The article deals with three questions which feed the debates on the school interdisciplinarity. Firstly, why promote interdisciplinarity in basic education? To answer this question requires to make a fast historical reminder of the emergence and the evolution of the interdisciplinarity in the field of education, so as to highlight three distinct readings of interdisciplinarity before presenting four arguments which show the relevance of its use in teaching. Secondly, how can interdisciplinary practices be fostered in education? The results of various works lead us to draw four strong trends, ideal-typical, to which the teachers use in their practices. Thirdly, what are the educational questions and issues raised by such practices, and what challenges must we address to make sure they are properly applied? We then suggest six principles by which to found and describe what we understand by school interdisciplinarity. A definition of the school interdisciplinarity is then moved forward. To conclude, we call back to mind a few aspects which appear essential when contemplating the use of interdisciplinarity in school, what leads us to place this one with regard to the transdisciplinarity.
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 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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".