Postscript: development ethics and the ethics of critique
Bibliographic record
Abstract
James Ferguson ends his account of The Anti-Politics Machine with a useful and very honest Epilogue which addresses the question ‘What is to be done?’ In the course of his discussion he makes a number of points that we find helpful. In particular, he warns against a form of romanticism that would turn the fieldworker into a hero or social activist. The truth is that most of us should not expect to make a difference. Social change is most often made slowly and in a non-linear fashion by the men and women who become the subjects of social science. Ferguson quotes Foucault to bolster this argument. ‘As Foucault remarked of the prisons, when the system is transformed, “it won't be because a plan of reform has found its way into the heads of the social workers; it will be when those who have to do with that … reality, all those people, have come into collision with each other and with themselves, run into dead-ends, problems and impossibilities, been through conflicts and confrontations; when critique has been played out in the real, not when reformers have realized their ideas”’ (Foucault 1981: 13, quoted in Ferguson 1990: 281). This view is largely consistent with the arguments we have developed here, although we would want to signal more clearly than Foucault the role that directed change can make, particularly when it is being pressed by the wealthy and the powerful.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 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".