2 Making Diversity in Rural Areas Visible: A Changing Perspective for Rural Schools in Québec
Bibliographic record
Abstract
used in the project "Keys for a Better Understanding of the Ethnocultural, Religious, and Linguistic Diversity in Schools." 1 The project and its typology bring to the forefront the different forms of diversity that exist within and across the 17 administrative regions of Québec. 2 Research on ethnocultural, religious, and linguistic diversity in Québec too often focuses only on the province's metropolitan regions-Montréal in particular-forgetting how, or even that, diversity manifests across the province.In this way, the diversity of metropolitan Québec is overvisibilized, while the diversity of nonmetropolitan Québec is invisibilized.The typology of the "Keys for a Better Understanding" project is worthwhile in that it investigates diversity in all of Québec's administrative regions, across the rural-urban spectrum.The "Keys for a Better Understanding" project is focused on diversity in the context of education.The typology reported here thus focuses on student-specific indicators of diversity, though it also looks at some indicators that are applicable to many contexts in Québec society.Because of its school-centred focus, however, we see this typology as a valuable tool in encouraging schools to take stock of their local diversity profiles as they design and deliver educational programs.It contributes to research
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".