Sen, Sankar (2010). * ENFORCING POLICE ACCOUNTABILITY THROUGH CIVILIAN OVERSIGHT
Bibliographic record
Abstract
The author, currently Senior Fellow at the Institute of Social Sciences, New Delhi has extensive experience as a member of the Indian Police Service, including a leadership role with the National Police Academy and has been affiliated with the National Human Rights Commission. All of this background provides an indication that the author comes to his subject with a combination of practical and theoretical experience. This publication attempts an international treatment of the broad topic of civilian oversight of policing and includes some detailed considerations in several jurisdictions. The book’s 15 chapters are devoted to observations that delve into the concept of police accountability. The author begins with reflections on the complications associated with policing in democratic societies. The thrust of Sen’s opening position is that democratic principles require police who are ‘accountable to multiple mechanisms’ (p. xiii). Immediately, however, it may be suggested that police organizations are actually responsible to people; their ‘professional’ colleagues, as well as, to their civilian governing authorities and not to the mechanisms in place that merely structure police oversight. Such mechanisms are merely the outward trappings of the actual core of accountability; the human dimension.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.013 |
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".