Bibliographic record
Abstract
There are a few solutions that could at least influence the decrease of GBVAW. One of them is raising awareness, informing people about their rights and opportunities to seek help and support is crucial. Victims of GBV often experience psychological problems which come together with stigma, shame and feeling of deserving the violence. Raising awareness could be done differently, using different platforms. The social media took its stand in solving important social issues. Celebrities, influencers, micro-influencers cover the topics of GBVAW and information on how to seek help. Undoubtedly, the work of NGOs and women shelters are important as never. Thus, Educational pages, just as other pragmatic apparatuses committed to handling lewd behavior, savagery as well as sexual orientation correspondence is a need. One of the ways to raise awareness and empower women and girls is to encourage victims to speak out and seek help both legal and psychological as well as the support of the family and friends. Therefore, one of the paramount importance is to erase the shame and stigma around the GBVAW. Undoubtedly, the legal framework shall support women in their intentions to seek help. Unfortunately, this became one of the most significant problems for several countries. In numerous states, enactment tending to sex based savagery against ladies is non-existent, deficient or ineffectively actualized. As well as seeking financing to sustain the women’s shelters and hotline. That became apparent during the lockdown, a lot of the women’s support centers had to close due to the shortage of financing. COVID-19 in addition to making a lot of harm to the state of GBVAW in the world at the same time brought new ideas to fight with it. Thus, during the lockdown, some of the police forces introduced special apps for reporting a GBVAW crime. Thus, an aggressor could not understand that a report had been made. This model of reporting is an excellent tool to seek help especially when a situation is highly dangerous.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".