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Record W4409172553 · doi:10.52783/jier.v5i2.2465

Inclusive Justice and Sustainable Legal Protections Against Domestic Violence: A Comparative Study of UK, Canada, and Australia

2025· article· en· W4409172553 on OpenAlexaboutno aff
Ayushi Aggarwal Asha

Bibliographic record

VenueJournal of Informatics Education and Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Issues in South Africa
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic JusticeDomestic violencePolitical scienceCriminologySociologyLawHuman factors and ergonomicsPoison controlEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

Domestic violence is a widespread issue that affects individuals across genders, yet in India, male victims remain primarily invisible due to societal stigma, media bias, and legal discrimination. Existing laws, such as the Protection of Women from Domestic Violence Act, 2005 (DV Act, 2005) and Sec. 84 and 85 BNS (Sec. 498A of IPC), exclusively protect women, leaving men without legal recourse. Media narratives reinforce gender stereotypes, portraying men solely as perpetrators, which contributes to underreporting, lack of institutional support, and severe psychological consequences for male victims. This research examines the legal, social, and psychological challenges faced by men and compares India’s gendered legal framework with gender-neutral domestic violence laws in the UK, Canada, and Australia. Furthermore, the study highlights how the exclusion of men from domestic violence protections directly impacts Sustainable Development Goals (SDGs) 3.4 and 3.8, which focus on reducing premature mortality from non-communicable diseases and achieving universal health coverage. High suicide rates among married men, limited access to mental health services, and the absence of dedicated support structures underscore the need for legal reforms, media accountability, and gender-inclusive policies. This paper advocates for gender-neutral domestic violence laws, improved mental healthcare access, and policy reforms to ensure a more just and equitable society.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.463
Teacher spread0.423 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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