Locating Democratic Citizenship in the Classroom: Engaging Canadian Teacher Codes of Ethics in the Quest to Understand What It Means to Teach Democratically
Bibliographic record
Abstract
Maxwell and Schwimmer (2016) found that within the Canadian teacher codes of ethics “the values of care and liberal democratic education were the most weakly represented values in the codes whereas the values related to reliability were the most dominant” (p. 477). Approaching the codes with a critical discourse analysis lens (Fairclough, 2003), the author concurs, finding a strong deontological emphasis in 11 of the 13 codes. The analysis of the codes is used as a gateway into the understanding of democratic ways and pedagogies within the classroom. The author proposes a both/and shift whereby “deontological” responsibilities are motivated by an “aspirational” focus (Maxwell & Schwimmer, 2016), thus moving codes of ethics and teaching/learning landscapes into more ethically democratic spaces. In the both/and context, individuals are valued before ideas untangled.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.051 | 0.041 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".