A Theoretically Critical Gaze on the Canadian Equal Marriage Debate: Breaking the Binaries
Bibliographic record
Abstract
Many competing and contradictory ideologies and theoretical perspectives have converged and diverged to influence and shape a complex and disparate LGBTQ cultural identity. Indeed, LGBTQ organizing around equality rights issues, particularly marriage, incurs a critique from inside “the movement” that is as heated and rancorous as that of the external debate. The visibility of gays and lesbians has increased dramatically during the equal marriage debate as those marrying have been both publicly vilified and hailed as heroes. The implications of same-sex marriage for the LGBTQ community are significant as its members face changing social roles. Moreover, as these couples chart new ground, they are without the signposts that surround heterosexual couples. The goal of this paper is to explore, from a critical theoretical lens, how we understand the nature and possibility of same-sex marriage and deconstruct the binaries associated with it.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".