Beyond the Limits: Conversation, Part I
Bibliographic record
Abstract
Tatiana Chudakova: I think one thing that I struggle with, and that struggle comes up in ethnographic writing, is the question of scale, along with medical anthropology's engagement with scale, and what does and does not count.There is a kind of unspoken romance of numbers, or a romance of statistics which we get with a Foucauldian lineage that I think speaks to, translates, hitches itself to an interest in public health and an interest in institutions.And so, once things are outside of these institutions and the optics of visibility that they confer, it becomes really difficult to both render that ethnographically, but also track it within the field work experience itself, if you don't start at the center, while doing justice to it in terms of what sort of ethnography is possible, or what sort of ethnographic writing is possible.If the story isn't a story about power writ large, then what sorts of writing is recognizable for both career purposes and for representational purposes becomes a really complicated question.At least for me.
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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 | 0.020 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.020 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.016 | 0.043 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".