Bibliographic record
Abstract
I was wrongAnthropologists like to tell stories about how they were wrong.These stories are part of our collective identity.Consider Briggs's (1970) classic ethnography, Never in Anger.Briggs tells a story about doing fieldwork in Nunavut (then the Canadian Northwest Territories) in the early 1960s.She showed anger on behalf of the Inuit group she was living with when they were wronged by a white outsider.To her surprise, her Inuit interlocutors reacted by ostracizing her for months.She describes feeling devastated, lonely, and depressed, but in slowly working her way back to personhood with her Inuit participants, she learned in a powerfully embodied fashion a core principle of their culture: that adults never show anger or make demands, and that to lose one's temper is to demonstrate a dangerous lack of control, even insanity.She documents how this emotional control is inculcated in children from a very young age.Thus her awful moment of social failure is transformed into a profound reflection on affect and socialization in Nunavut culture.The resulting ethnography became one of that era's most famous.Even when our failures and mistakes are less obvious than Briggs's and don't threaten to derail our research projects, many ethnographic writings reflect on moments when the author realized they had brought a bad assumption into the field with them and, as a result, learned something from their research participants-something that shifted their methodological or theoretical approach, or changed what they were studying.These stories are particularly prevalent among graduate students returning from their first fieldwork experiences.The stories go something like this: I went to the field planning to study x, and when I was in the field, I listened to my interlocutors and realized that what I really needed to study was y.For example, in her book Food, Sex and Pollution, Meigs (1984) writes about going to the field intending to study divorce, but when she gets there, everyone seems bored and uninterested when she asks about the topic.Instead, they want to talk to her about sex, gender, and the pollution rules that govern what they can and can't eat.It's such a collective concern that she describes their interest in the topic as a "religion."Responding to this, she changes her research topic and commits to addressing her research participants' interests, letting them chart the direction of her work.Briggs tells a similar story: she goes to the Inuit planning to study shamanism, but when she arrives, she finds that the small group she is living with have converted to Christianity and don't want to discuss their "pagan" past.That, combined with her attempts to process the social ostracism caused by her 44
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.413 | 0.280 |
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".