Bibliographic record
Abstract
Based on the secondary sources and interviews, this paper gives an account of hartal and similar political activities, examining the economic and social impacts of such activities on the economy of Bangladesh and the management of the companies in that country. As hartal and associated activities are not well defined in literature, this study begins by giving a brief definition and description of those activities. The findings of the study suggest that these activities are organized to ensure freedom of assembly, expression of opinion, and political rights by raising protests against certain government actions and policies. However, in reality, and especially during the last two decades, much of these activities have been used as a vindictive movement against the political party in the helm of the regime. The study also found that hartal and similar activities have resulted in colossal economic losses of work, working hours, business management, industrial output, business capital, property, and human life, as well as visible and non-visible social losses, such as human distress, loss of human life, uncertainly, chaos, hatred, disunity among people, and erosion of the national image. Finally, this study found that people engaged in economic activities dislike such political activities, considering them as unnecessary evils, and want political parties to work on creating alternative, peaceful action programs. The paper finally gives some suggestions to solve the problem of hartal and relieve the miseries that come along with it.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".