Sex worker rights are human rights: an approach to solidaristic normative theory
Bibliographic record
Abstract
In this article, we argue for solidaristic normative theorizing, which entails engaging in sustained dialogue with people who experience oppression and working with them to advance their struggles for justice. We draw guidance from Indigenous, Black, and Latine scholars, who have long done this type of work. We develop and apply principles of inductive recursivity, which privileges the insights of those who experience oppression; comprehensiveness of research materials and attentiveness to power; skeptical but empathetic scrutiny when assessing research materials; and accountability through reflexivity and ongoing relationships with oppressed individuals, groups, and communities. With reference to the struggles of major sex worker rights organizations in Canada and the United States to address the harms associated with criminalization, we outline our approach to theory building and illustrate what it looks like in practice. By developing a conceptual link between the everyday harms faced by sex workers, due in large part to criminal laws around their professional activities, and the deeper ontological and epistemological injustices against them, we hope that our theorizing contributes – even in a small way – to public understanding of the depth of the oppressive governance regime of criminalization and the importance of dismantling it.
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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 | 0.022 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.100 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".