Modèle pour contrôler la qualité adadémique des textes de l'instruction à distance
Bibliographic record
Abstract
Dans les systemes de de l’education a distance, le contact de l’eleve avec le savoir s’accomplit sans qu’il n’y ait necessairement un contact direct avec l’educateur. La communication peut passer en utilsant divers moyens, tells que: le telephone, le courier (postal et electronique), le text imprime, le text video, les moyens audio-visuels. Ceci article propose un modele methodologique delimite par des criteres permettant l’evaluation des materiels imprimes et par consequent de leur forme et de leur production. Dans de tells criteres on considere les aspects didactiques du contenu, de la communication, les aspects technico-graphiques ainsi que les variables et constants qui en derivent. Abstract In distance education systems, contact between student and knowledge is made without there necessarily being direct contact by the educator. Communication may occur using several media, such as telephone, mail (postal and electronic), written text, videotext, and audio visual materials. This creates the need for a system of control, which, among other things, determines the quality of these instructional media. In this article we propose a methodological model based on criteria that permit evaluation of the written materials and also their design and production. Such criteria include didactic aspects of the content, communication, and graphic and visual aspects as well as variables and attributes derived therefrom.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".