MétaCan
Menu
Back to cohort
Record W6964263132 · doi:10.25384/sage.c.5484705.v1

Legacy of Honor and Violence: An Analysis of Factors Responsible for Honor Killings in Afghanistan, Canada, India, and Pakistan as Discussed in Selected Documentaries on Real Cases

2021· other· en· W6964263132 on OpenAlexaboutno aff

Bibliographic record

VenueSage Journals Data · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsHonorEmancipationPunishment (psychology)GentryResistance (ecology)

Abstract

fetched live from OpenAlex

The present study scrutinizes the cases of honor killings in Afghanistan, India, Pakistan, and Canada through selected documentary films. The case focuses on the social, moral, and religious aspects that coerce some people to take the lives of their own family members in case they defy norms. The documentaries chosen as case studies provide the perspectives of both the victims and the victimizers regarding the concepts of honor, dishonor, and honor killings. People in certain societies reject progressive new thought as attempts to contaminate their perceived cultural purity. People from these communities who try to assimilate liberal ideas are often shunned, especially when the emancipation of women is concerned. Even the seemingly progressive males are very unforgiving about the female members of their families embracing the modern ways of life. The women who try to defy set traditions are branded as being rebellious and are punished to serve as a precedent for future rebellions by women and to save society from their alleged bad influence. In some patriarchal societies, women are seen as the preservers of the family’s honor, and their conduct often reflects the family’s culture, morality, and ethics. Any lapse on a woman’s part allegedly taints the family’s name, and punishment must be given to the erring party to restore the family’s honor. The case also studies the influence of society as a compelling factor in honor killings.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.349
Teacher spread0.318 · 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.

Study designObservational
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueSage Journals DataFrench-language works237,207