Media coverage of violence against women in India: a systematic study of a high profile rape case
Bibliographic record
Abstract
BACKGROUND: On December 16, 2012 a 23 year old female was gang-raped on a bus in Delhi. We systematically reviewed professional online media sources used to inform the timing, breadth of coverage, opinions and consistency in the depiction of events surrounding the gang-rape. METHODS: We searched two news databases (LexisNexis Academic and Factivia) and individual newspapers for English-language published media reports covering the gang-rape. Two reviewers screened the media reports and extracted data regarding the time, location and content of each report. Results were summarized qualitatively. RESULTS: We identified 534 published media reports. Of these, 351 met our eligibility criteria. Based on a time chart, the total number of reports published increased steadily through December, but plateaued to a steady rate of articles per day by the first week of January. Content analysis revealed significant discrepancies between various media reports. From the 57 articles which discussed opinions about the victim, 56% applauded her bravery, 40% discussed outrage over the events and 11% discussed cases of victim-blaming. CONCLUSIONS: The global media response of the December 16th gang-rape in India resulted in highly inconsistent depiction of the events. These findings suggest that although the spread of information through media is fast, it has major limitations.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".