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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads agree on what is shown here.
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".