How does the Use of Cellular Phone Commit Violence Against Women
Bibliographic record
Abstract
Over the years we see the less participation of women in the technological world especially in the developing nations like ours. Owing to gender gap and other social constrains, technology specially the use of cellular phone is biased towards young boys. In the context of Bangladesh the mobile technology is used by some group of people to perpetrate violence against women. This research monograph is been composed to examine how does violence against women commit by the use of cellular technology. The entire paper has been decorated in six chapters. The 1st chapter portrays the forwarding of the research topic which includes the research objectives and literature review. The second chapter focuses the conceptual framework linked to the research title. The 3rs chapter deals with the methodological arrangement. Here I have endeavored to go after the qualitative research technique. However the next chapter spotlights the profile of the responded which has assisted me to write up the further analysis. The fifth chapter analyses how does technology is responded in perpetrating violence against women. At the end of the day the final chapter deals with the recommendation and concluding remarks. Key words: Women; Cellular phone; Qualitative research; Violence
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".