Beyond the Limits: Conversation, Part III
Bibliographic record
Abstract
Tatiana Chudakova: My work very much tackles multiplicity, in the classical medical pluralism way as: "Okay, this is the basic feature of what this [therapeutic] life world is like."It's the multiple as assumed and implied, and experienced as the state of medicine or healing… And maybe what my piece was trying to do is that when we encounter multiplicity within medical anthropology, there is always this push to find the common variable or the explanatory ground.What is that relationship between figure and ground?What is being privileged when we talk about multiplicity?What are the slippery slopes where we actually sneak in meta-level explanations through the back door?And how does one toggle between them, especially if they work at scale?That's something I'm constantly thinking about when engaged in ethnographic writing. Abigail Neely:Like Tatiana, I wonder in some ways if this notion of multiplicity (and I've also wondered this about Feierman's medical pluralism) is a way to allow biomedicine to stay in there?Following this, it seems that perhaps biomedicine is trying to foreclose everything else and stay at the center of a lot of medical anthropology.Or, are we allowing biomedicine to foreclose other forms of healing?How do we think critically about that as a limit?Tatiana Chudakova: At least in the context where I work, nobody can agree on what biomedicine is in the first place.So, assuming that these categories exist out there as stable things that we can relate to and seamlessly translate to each other is another thing that I'd like to see challenged in the future of anthropology.It's a taxonomic fallacy.We need to make sure that we are not reifying North American categories, to put it very bluntly, of what something is.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.043 | 0.051 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.016 | 0.034 |
| 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".