Bibliographic record
Abstract
Introduction We have said that one aim of this book is to consider how differently placed men and women see the state in rural India. Some of these individuals will be employees of the state, or external advisers to the Government of India and its constituent states and Union territories, although many more will be farmers or labourers. Some will be political fixers and members of the Backward Classes, while others will be farmers, Class IV government servants and adivasis at the same time. But what does it mean to talk about ‘seeing the state’? We are used to the idea of the state seeing its population or citizenry. Visuality is at the heart of many theories of power and governmentality. Michel Foucault, most notably, has shown how the birth of modern forms of education and welfare provision corresponds to the emergence of biopolitics as a ‘form of politics entailing the administration of the processes of life of populations’ (Dean 1999: 98). Populations emerge when changes in working practices give rise to economic government and the discipline of political economy, and they get bounded by new exercises in mapping and measurement, including the production of censuses, cadastral surveys and expeditions. Biopolitics then refers to those government interventions that seek to improve the quality of a population as a whole, and these procedures produce that which we name the state as the effect of these interventions.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".