Bibliographic record
Abstract
This chapter is a historical policy study that examines a particular moment in the development of the Canadian diversity policy web. Starting from the position that policy is the result of a complex set of interactions among several state and nonstate actors, we use the metaphor of the web to examine the relationship between and among policy statements and actions in a particular field. 1 Policy is thus understood as more than a single authoritative text, and the notion of “policy actors” replaces the traditional term “policy makers” as we examine the roles of multiple players in the field. We are interested in understanding the complexity that lies behind the creation and ongoing re-creation of official policies. We believe that at its best, policy can be the result of and catalyst to public dialogue about issues that are of central importance to a society. To this end, our work seeks to find ways of making the policy process more open and democratic. Because of our own backgrounds and commitments, we focus particularly on the study of policies addressing diversity and equity. We have found that the 1940s was a pivotal time in the development of diversity and equity policies in Canada. So, as we focus in this volume on transformations in education, it seems most appropriate to concentrate on this period. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.037 | 0.015 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".