MGR Quarterly Infographics Report: April-June 2023
Bibliographic record
Abstract
MGR recorded 5115 violent incidents from April to June 2023, mostly triggered by politics, access to resources, and other socio-economic factors. More than 858 deaths and 4462 injuries have been recorded from these incidents. The highest number of violent incidents have been recorded in the form of clashes and attacks (1347). Some 1026 incidents are directly or indirectly political violence which resulted in 82 deaths. Geographically, Dhaka (1045) scores the highest number of violence followed by Chittagong (995), Rajshahi (784), and Khulna (705). There were 581 protests and demonstrations and at least 373 were triggered by politics. While some 34.74% of political violence contributed by Bangladesh Awami League & affiliates, 14.95% contributed by the Bangladesh Nationalist Party (BNP). Intra-party violence within the Awami League maintains a significant surge in the second quarter of 2023. Whereas 62% incidents were rural, 38% violence incidents took place in urban areas in this quarter. During the second quarter, Bangladesh experienced a significant increase in incidents of student violence, with a total of 208 cases reported across different regions. A notable finding was that approximately 37% of political violence were directly linked to student or campus-related conflicts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.244 | 0.168 |
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".