Review: Didier Fassin (Ed.) (2017). If Truth Be Told: The Politics of Public Ethnography
Bibliographic record
Abstract
Didier FASSIN's new edited collection brings together public anthropologists and sociologists who reflect on the challenges and stakes related to the dissemination, promotion, reception, and utilization of their ethnographic research. The book centers on two questions. First, what kind of difference does ethnography make when research findings are transported into the public domain? Second, what happens in this encounter between ethnographers and their publics? In order to address these questions, contributors offer ethnographic accounts of the public reception of their ethnographic research. As the book illustrates the variety of public encounters and diverse issues that arise in the process of ethnography "going public," it offers valuable contributions to multidisciplinary discussions related to ethnographers' political and ethical commitments.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.015 |
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".