Making Our Judgments Right: Ethics of Corporal Punishment in Indian Schools
Bibliographic record
Abstract
The case Ayub Khan v. Municipal Corporation of Delhi Schools is one of the landmark cases for promoting a conversation on ethics of corporal punishment in Indian society. Based on literature synthesis and the critical examination of the Court case using ethical frameworks (Stefkovich, 2006; Stefkovich & Shapiro, 2010) this paper argues that law alone may not preclude teachers from using an age old practice such as corporal punishment. Furthermore using literature related to the Court case as secondary data sources, the paper argues that teachers trained in incorporating ethical paradigms in their decision making presents best chances for effective implementation of laws against corporal punishment in Indian schools.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.062 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".