The VELTIC hypermedia system: For school principals in the promotion of the French language in Canada
Bibliographic record
Abstract
In the Canadian context, the survival of French as a minority language, with the related issues of linguistic and cultural vitality, is a major concern. TheFrench-language minority school systems in Canada are in a constant state of change because of the increasing cultural diversity of their students, their task in promoting the French language in an official-language minority setting (Gerin-Lajoie, 2006). Thus, are school principals well equipped to contribute to the promotion of the French language and Francophone identity construction? Few studies have taken up the challenge of investigating French-language vitalization through ethno-linguistic practices that support school principals in the promotion of the French language and Francophone identity construction. The goal of our research on the development of pedagogical objects (Van der Maren, 2003) consists of creating a catalogue of ethno-linguistic vitalization practices (elv practices) from principals, to distribute them in a hypermedia system of resources and to evaluate them. A pan-Canadian study was undertaken to document elv practices among 234 school principals. According to preliminary results, the majority of principals consider it useful to collect, in one site, all the elv practices put in place by other French school principals. This paper will present a five step approach for developing pedagogical objects in a hypermedia system of resources (http://veltic.ca).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".