The Distinction between Morals and Ethics: Discourses of Sex that Reciprocate with Studentsâ Learning Needs within the Toronto District School Board and other Secular School Boards of Ontario
Bibliographic record
Abstract
By analyzing surveys, census data, policies and curriculum, it is demonstrated that the Toronto District School Board’s policies for equitable, anti-heterosexist, and anti-homophobic curriculum become stymied by how students and sex are routinely treated as subjects of moral control in curriculum. According to Gilles Deleuze's (1988) interpretation of Baruch Spinoza's (1632-1677) philosophical works, the distinction between morals and ethics is also the difference between slavery and freedom. Together with theoretical perspectives of sex and sexuality from Michel Foucault, Judith Butler and Gayle Rubin, the distinction between morals and ethics works to specify how particular discourses of sex can work to enslave or to empower students. Comprehension and circulation of the distinction between morals and ethics is proposed to increase the potential for curriculum to reciprocate with students’ individual learning needs, support the free and autonomous organization of desire, and promote the possibility of a democratic, inclusive, pluralistic, and secular society.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.040 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".