The dark side of technology: an editorial on coercive control in the digital age
Bibliographic record
Abstract
i a u xBien que la technologie numérique bénéficie d'une réputation impressionnante, fournissant une communication instantanée et un accès à l'information à une échelle inégalée, elle entraîne inévitablement des répercussions ; y compris le fait d'être devenu une dimension de la violence entre partenaires intimes (VEPI) contemporaine connue sous le nom de contrôle coercitif numérique (CCN).CCN implique l'exploitation des technologies numériques quotidiennes pour intimider, isoler, faire honte, surveiller et contrôler délibérément -principalement des femmesdans des situations de VEPI ; généralement sous la forme de harcèlement en ligne, d'abus sexuels basés sur l'image, de surveillance électronique et de harcèlement criminel, d'usurpation d'identité et de cyberfraude.Cette manifestation de la VEPI a eu un impact profond sur la vie des survivants, entachant leur vie sociale et professionnelle, ainsi que leur santé émotionnelle et physique de base -ce qui signifie un facteur de risque croissant d'homicide domestique.Alors que les chercheurs et les gouvernements internationaux ouvrent la voie dans ce domaine de travail, l'Amérique du Nord a été lente à suivre, avec la plus grande réponse jusqu'à présent émergeant au niveau de la base et à but non lucratif.Sans attention immédiate, la technologie continuera de s'entrelacer avec nos vies sociales et professionnelles à des rythmes rapides et en évolution, devenant peut-être l'une des manifestations les plus dominantes de la VEPI que le monde ait jamais connue. R É S U M ÉWhile digital technology boasts an impressive reputation, providing instantaneous communication and access to information on an unmatched scale, it inevitably has repercussions; including having become a dimension of contemporary intimate partner violence (IPV) known as Digital Coercive Control (DCC).DCC entails the exploitation of everyday digital technologies to deliberately intimidate, isolate, shame, surveil, and control -predominantly women -in situations of IPV; commonly carried out in the forms of online harassment, image-based sexual abuse, electronic monitoring and stalking, impersonation, and cyber fraud.This manifestation of IPV has had a profound impact on the lives of survivors, tainting their social and professional lives, along with their emotional and physical health -further signifying a growing risk factor for domestic homicide.While international researchers and governments are paving the way in this line of work, Canada has been slow to follow, with the greatest response thus far emerging at the non-profit level.Without immediate attention, technology will continue to intertwine with our social and professional lives at rapid and evolving rates, perhaps becoming one of the most dominant manifestations of IPV the world has witnessed yet.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".