Dr Strangelove, or How I Learned to Stop Worrying about Methodology and Love Writing
Bibliographic record
Abstract
This rejoinder pays special attention to two of Jason Rancatore’s main points of criticism: that I advance a purist notion of ethnography and that ethnography is a data-collection method like any other. Firstly, I defend my earlier interdisciplinary reading of ethnography, arguing that, while anthropology does not maintain a monopoly over the ‘pure’ or ‘proper’ dispensation of ethnography, the history and complexity of this practice cannot be grasped in its entirety unless we engage its ‘home field’ of anthropology. Secondly, I approach ethnography as a critique of the way in which knowledge is commonly produced and communicated within social science research. Rather than obsessing over questions of research design, ethnography is an exercise in being truthful about the distance we travel from research questions to finished manuscript, with all its doubts, epiphanies and improvisations. If the case of ethnography and IR is a strange one, as Rancatore suggests, it is because the contribution of ethnography continues to be assessed in terms of the purchase power it has for disciplinarity and not in light of the avenues it opens for making academic writing useful to a wider variety of purposes and audiences.
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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 | 0.033 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.036 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".