Production semi-automatisée d’une carte conceptuelle en science et technologie
Bibliographic record
Abstract
Cet article décrit la conception et la mise à l’essai d’un programme informatique de production semi-automatisée de cartes conceptuelles. Cette technique dérivée de l’extraction d’information a pour but de produire une représentation simple et signifiante du contenu d’un ou de plusieurs textes sous forme d’un schéma de connaissances. Le programme a été mis à l’essai à partir de deux corpus de textes choisis pour couvrir deux thèmes du Programme de formation de l’école québécoise de quatrième secondaire en science et technologie. Les résultats de l’étude sont très encourageants et montrent le potentiel d’une telle approche pour faciliter la construction d’une carte conceptuelle, une tâche qui est généralement réalisée par des humains. Les résultats suscitent également quelques réflexions quant aux modalités courantes d’élaboration et d’évaluation de cartes conceptuelles.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".