Researchers’ identities and their consequences for fieldwork. The methodological and scientific implications of gender, illicit practices and disclosure.
Bibliographic record
Abstract
Is it possible to conduct rigorous, reflexive social science research without revealing something of oneself? This is the question I propose to address in this article, analysing the influence that my personal identities as a researcher, my gender identity, and my experiences in the drug scene had on my access to this field, the nature of the data gathered and the process of analysing that data. As a woman who uses drugs writing a research thesis on female drug users in Bordeaux and Montreal, I was very well integrated into the Bordeaux field context and much less so in Montreal, resulting in an asymmetrical comparison. This asymmetry cannot be explained without taking into account my own personal experience among drug users. My gender identity also shaped the data-gathering process: during my field research I had to contend with sexual harassment, as well as being on the receiving end of stereotypes undermining my credibility, all the while dealing with my own emotional trauma linked to past experiences of sexual violence. The resulting research is a form of “delinquent ethnography”, whose participatory dimension has clear scientific and methodological advantages, but which also raises important ethical questions. This article concludes with some proposals for protecting women researchers from gender violence and inequality in their fieldwork.
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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 | 0.376 | 0.387 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.022 | 0.067 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.005 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".