Educational Pluralism and Tolerance
Bibliographic record
Abstract
The issue of intolerance is increasingly being brought to our attention. (See, for example, a recent publication by Jay Newman, Foundations of Religious Tolerance, Toronto: University of Toronto Press, 1982). A task force recently appointed by the Government of Alberta in response to the Keegstra affair was assigned to study the problem of intolerance within our education system and in our society generally. The committee's report gives expression to a very commonly held position, namely, that the key to fostering tolerance in a society is to maintain a strong and uniform public school system, and that the proliferation of private/alternative/separate schools will foster intolerance in our society. The purpose of this article is to refute this belief by clarifying the concept of "tolerance,'' uncovering some problematic assumptions underlying the opposition to educational pluralism, and considering relevant empirical evidence.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.095 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| 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".