Does Character Education Really Support Citizenship Education? Examining the Claims of an Ontario Policy
Bibliographic record
Abstract
The claim that the character education policy of a school board in Ontario, Canada supports citizenship education is examined. 181 documents were analyzed to determine the ways the policy supports and/or undermines citizenship education’s goal to prepare students to become “knowledgeable individuals committed to active participation in a pluralist society ” (Sears, Clarke, and Hughes, 2000, p. 153). The findings show that the policy encourages students to acquire specific values, behaviours, and interpersonal skills rather than conceptual or situational knowledge. While the policy encourages active citizenship by promoting the development of decision-making, conflict resolution, and communication skills, it emphasizes participation in activities that support rather than challenge the status quo. The policy also offers some support for developing students ’ commitment to pluralism, but its narrow definition of diversity and emphasis on shared values, behaviour, and language contradict these efforts. I conclude that the policy supports citizenship education that adopts an assimilationist conception of social cohesion and/or social initiation as its purpose(s).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.019 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".