MGR Quarterly Infographics Report: October – December, 2023
Bibliographic record
Abstract
MGR recorded 5121 violent incidents in during October to December 2023, mostly triggered by politics, access to resources, and other socio-economic factors. More than 659 deaths and 4133 injuries have been recorded from these incidents. The highest number of violent incidents have been recorded in the form of clashes and attacks (1456). Some 2571 incidents are directly political violence, protests and arrests which resulted in 77 deaths. Geographically, Dhaka (1110) scores the highest number of violence followed by Chittagong (1027), Rajshahi (809) and Barishal (638). There were 1051 protests and demonstrations and at least 887 were triggered by politics. While some 24.93% of political violence contributed by Bangladesh Awami League & affiliates, 24.70% contributed by the Bangladesh Nationalist Party (BNP). Intrq-party violence within the Awami League showed a significant surge during October-December 2023 as Awami League to encourage 'independents' for 'participatory' polls. Whereas 49% incidents were rural, 51% violence incidents took place in urban areas in this quarter. In this quarter, student violence decreased with a total of 80 cases reported across different regions. Between 28 October to 30 November 2023 during the protests and hartal of Bangladesh Nationalist Party (BNP), there were a total of 564 incidents of political violence across Bangladesh, leading to 29 deaths, 1343 injuries, and damage to 479 properties.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.341 | 0.280 |
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".