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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 OpenAlex
Ghayda Hassan, Jihan Rabah, Pablo Madriaza, Sébastien Brouillette‐Alarie, Eugene Borokhovski, David Pickup, Wynnpaul Varela, Melina Girard, Loïc Durocher‐Corfa, Emmanuel Danis

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.

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

Abstract 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.

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)0.012
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
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 head0.357
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