Bibliographic record
Abstract
While qualitative significance can be attributed to the emergence of the first instances of sex worker union organisation in Australia, Britain, Germany, the Netherlands and the US in regard of the relative ‘underdevelopment’ of comparable organisation in Canada and New Zealand (and elsewhere), this cannot be correctly done without at the same time also locating this phenomenon in the context of the quantitative sparseness of the overall extent of this development. Although speaking of COYOTE and PONY in the 1980s, Plachy and Ridgeway’s (1996:34) observation is equally applicable to sex worker union organisations of the 1990s and 2000s. They commented: ‘The reality beyond this [sex work] debate is that only a tiny minority of sex workers have ever heard of these organisations’. Alternatively, and speaking of the 1990s, Altman (2001:102) argued: ‘Most people who engage in sex for money have no sense of this [the sex work discourse] comprising their central identity, and they may well be repelled by attempts to organize around an identity they would strongly reject’. Similar points about the degree of representative-ness by those who subjectively see themselves as sex workers for all those who are objectively sex workers have been raised by others (e.g. Bernstein 1999:111; Zatz 1997:283). Therefore, and in conjunction with the previous chapter, this chapter examines the forces and processes that have served to act as barriers to the unionisation and union organisation of sex workers. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".