Turning ethnography on its head in research about internet sexual offending
Bibliographic record
Abstract
This paper focuses on methods from a 17-month ethnography in UK group programmes for 81 users of online child sexual exploitation material. It analyses the affordances and restrictions of conducting research in a defined programme/service and professional setting segmented from participants’ everyday lives, as well as procedures put in place for enhanced participant anonymity, confidentiality, privacy, and boundaries. The article demonstrates that the setting and methods resulted in limitations that were simultaneously assets, arguing that information about online sexual offending would likely not have been gleaned otherwise. As a result, the typical ethnographic trajectory was turned on its head: sensitive information rarely told to anyone was divulged in detail, while basic elements about participants, their social networks, and their lives were not. This invites researchers to consider which tenets of ethnography are immovable versus flexible, and the types of information that can/should be obtained through certain methods for specific topics.
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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 | 0.059 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".