The Education of Immigrant and Refugee Students in NL: What Philosophy of Education Might Say
Bibliographic record
Abstract
Philosophy of education is variously understood; to some, it is the discipline in education that gets at ‘big questions’ or ‘first things,’ i.e. metaphysical and moral grounds, principles, and rules that purport to provide a foundation for teaching and learning, the curriculum, and schools. To others, it is a system that helps us to interpret scientific and social-scientific findings and put these in the context of persons, community, society, and (especially) politics. These two positions (and there are others) are not mutually exclusive. In this discussion, I will draw on both of these (disciplines and systems) as I discuss the role philosophy of education might play in discussions of the education of immigrant and refugee students in Newfoundland and Labrador (hereafter NL).
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.048 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| 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".