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Record W4282961510 · doi:10.1002/cl2.1245

PROTOCOL: Hate online and in traditional media: A systematic review of the evidence for associations or impacts on individuals, audiences, and communities

2022· review· en· W4282961510 on OpenAlexaff
Ghayda Hassan, Jihan Rabah, Pablo Madriaza, Sébastien Brouillette‐Alarie, Eugene Borokhovski, David Pickup, Wynnpaul Varela, Melina Girard, Loïc Durocher‐Corfa, Emmanuel Danis

Bibliographic record

VenueCampbell Systematic Reviews · 2022
Typereview
Languageen
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsConcordia UniversityUniversité du Québec à Montréal
Fundersnot available
KeywordsProtocol (science)Promotion (chess)IdeologyPsychologyMedia contentConsumption (sociology)Empirical evidenceSocial psychologyEmpirical researchSystematic reviewPublic relationsPolitical scienceSociologySocial scienceComputer scienceMEDLINELawMedicineAlternative medicineMultimedia

Abstract

fetched live from OpenAlex

This is the protocol for a Campbell systematic review: The objectives are as follows: (1) to critically and systematically synthesize the empirical evidence on the effects or impacts of exposure to or consumption, active search, or promotion of hate content online or in traditional media; (2) to describe how the characteristics of hate (e.g., type of content, ideologies, severity, type of platform) impact the documented effects; (3) to collect and identify the role of contextual variables (e.g., individual traits, age, gender, socio-economic background) on the documented effects; (4) to collect and produce a meaningful classification of outcomes; and (5) to identify gaps and limitations in the research and related policy documents.

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)0.160
metaresearch head score (Gemma)0.257
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.160
Threshold uncertainty score0.848

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1600.257
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0140.011
Bibliometrics0.0160.016
Science and technology studies0.0050.007
Scholarly communication0.0090.010
Open science0.0050.007
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0850.014

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 head0.357
GPT teacher head0.403
Teacher spread0.046 · 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 designSystematic review
Domainnot available
GenreProtocol

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

Citations8
Published2022
Admission routes1
Has abstractyes

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