Bibliographic record
Abstract
In this article we draw upon data from a large-scale mixed methods investigation of clients of commercial sex workers in Canada to illustrate the potential value that understanding and integrating computer and network technology has for enhancing access to, and participation from, marginalized and stigmatized populations. In particular, we present qualitative data from analysis of our research field notes as well as an analysis of quantitative data from response monitoring and feedback features built into the actual data collection process to help support our argument that, for some populations, network technology–based recruitment strategies should be recognized as the preferred recruitment option. In addition, we discuss the potential utility and application of viral solicitation, a newly emerging computer network-based nonprobability technique, for contacting and securing the participation of stigmatized and marginalized research participants. Our recruitment of sex buyers through web-based listserves was the most successful participant solicitation strategy, generating 63.18% ( n = 544) of our survey respondents. Conventional recruitment (advertising in print-based media and in adult-oriented businesses) generated few participants (2.90%, n = 25). Viral solicitation acted as an important low-cost supplemental means of recruitment, generating a further 164 survey participants (19.05% of survey participants).
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 | 0.027 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".