Bibliographic record
Abstract
This chapter is a reflection on the methods and ethics of doing fieldwork with highly vulnerable children. Fired up with good intentions, a knowledge of children’s rights, and a belief in the necessity of child-focused anthropology, a quarter of a century ago I went to Thailand with the aim of working with child prostitutes. My naive intention was to explore the children’s lives and suggest solutions to the problems they faced. I found the reality very different from my expectations, and therefore this chapter looks at the lacuna between my theoretical knowledge of ethics and the difficulties I had making sense of them on the ground. Here I discuss how my feelings about this work have changed over time and I interrogate the mistakes I made during both fieldwork and ‘writing up’. The chapter looks at the strengths and weaknesses of child-centred anthropology, raising questions about how to interpret children’s voices when they do not fit with one’s own worldview or morality. In doing so it looks at the lifelong impacts such research can have on both researcher and researched and questions the purpose of such research and whose needs it fulfils
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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 | 0.046 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.090 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.016 | 0.045 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".