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Record W4416360787 · doi:10.1080/17512786.2025.2592060

The Work of Sexual Violence Reporting

2025· article· en· W4416360787 on OpenAlexfundaboutno aff
Nelanthi Hewa

Bibliographic record

VenueJournalism Practice · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsJournalismWork (physics)Sexual violenceStructuringDigital mediaAgency (philosophy)

Abstract

fetched live from OpenAlex

This article argues that exploitative pay, the whiteness of the journalism labor force, and the intensification of digital journalistic work all demand that sexual violence news coverage be considered in new ways that highlight the changing conditions of work and the growing role of digital technologies. Through an analysis of interviews with Canadian journalists who have investigated sexual violence, I find that the material realities and labor conditions structuring sexual violence news coverage are central to the experiences of journalists doing this work. For new, contract, or freelance journalists—particularly if they are journalists of color—the pressures of the industry are severe and formal institutional support is rare. The interview data I present demonstrates that an overriding sense of scarcity, the complex role of metrics, and the unequal demands of time and pressures to produce shape the fabric of working journalists’ lives and limit journalists’ capacity to report with care.

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 imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.024
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.039
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.005
Science and technology studies0.0190.043
Scholarly communication0.0170.007
Open science0.0020.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.055
GPT teacher head0.431
Teacher spread0.376 · 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 source (direct Gemma or distilled Codex), 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 routes2
Has abstractyes

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