Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As in China, many of India’s remedied wrongful convictions involved police-induced false confessions. They likely reveal only a small “tip of the iceberg,” given the many missing remedied wrongful convictions found in other jurisdictions. Indian appellate courts are not reluctant to overturn convictions in part because of the absence of jury trials. India’s record of remedied wrongful convictions supports the abolition of the death penalty, with no exception for terrorism cases. Criminal laws enacted by the Modi government at the end of 2023 have increased the risk of wrongful convictions by, for example, increasing police custody, forensic investigations and restricting executive clemency. The 2023 laws did not implement the 2018 Law Commission recommendations to provide compensation for both the wrongfully detained and the wrongfully convicted, even though three-quarters of prisoners in India are awaiting trial.. Finally, possible futures for innocence projects and innocence movements in India are explored, with attention to the need to be sensitive to local conditions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it