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Record W4313494266 · doi:10.1177/17416590221142762

Looking beyond the law to respond to technology-facilitated violence and bullying: Lessons learned from Nova Scotia’s CyberScan unit

2023· article· en· W4313494266 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCrime Media Culture An International Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicStalking, Cyberstalking, and Harassment
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsNova scotiaFraming (construction)Law enforcementUnit (ring theory)Political scienceEnforcementPublic relationsCriminologyLawPsychologyEngineering ethicsSociologyEngineering

Abstract

fetched live from OpenAlex

Legal remedies in response to technology-facilitated violence and bullying (TFVB) have often overshadowed the creation of alternative responses. While the framing of law as the most impactful remedy can result in the false belief that this issue has been adequately dealt with through legal regulation, in practice legal options are not utilized by the majority of those harmed by TFVB, do not provide many of the core supports that targets of TFVB seek to access, and offer limited possibilities for prevention and culture change. Responding to growing demands for alternative responses to TFVB, this article provides an analysis of the province of Nova Scotia’s CyberScan unit—a government enforcement unit offering alternative supports and responses to TFVB—to explore the efficacy of implementing alternative responses to TFVB in practice.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.089
GPT teacher head0.395
Teacher spread0.306 · 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