GEORGE J. BEDARDEDUCATIONAL POLICY / January-March 1999 Constructing Knowledge: Realist and Radical Learning Within a Canadian
Bibliographic record
Abstract
A common response to educational crises is for governments to establish blue-ribbon panels. These panels, or commissions in Canada, are often given the mandate to solicit expert opinion, conduct research, develop a cogent synthesis of the findings, and submit solutions to policy makers. This article examines the work of an Ontario royal commission operating in a highly charged political setting from a constructivist perspective. Key issues include how commission members construct knowledge about educational reform, how they perceive their multiple purposes, and how research should be undertaken and recom-mendations formulated. CANADIAN COMMISSIONS for the determination of policy, along with their British and American counterparts, are ad hoc, temporary bodies estab-lished to provide governments with timely policy advice about vexing, nonroutine topics that are usually defined in an official mandate. In some coun-tries, such bodies as blue-ribbon panels, governmental special committees, summits, and task forces carry out similar functions. A commission’s advice usually takes the form of a written report with recommendations for policy action. Commissions usually place great store on either generating or synthesiz-ing large amounts of research about the topics they are investigating, and thus they are often considered to be authoritative sources on particular policy problems. Even if recommendations do not find favor with the government of the day, a commission’s research and argumentation may take on strategic
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.040 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".