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Record W4286457280 · doi:10.1075/ps.21014.bar

“They fabricated lies against us and described us in the harshest of ways”

2022· article· en· W4286457280 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.

Bibliographic record

VenuePragmatics and Society · 2022
Typearticle
Languageen
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTransitive relationFocus (optics)PublishingSociologyMedia studiesSocial mediaScale (ratio)IslamState (computer science)Computer scienceLinguisticsWorld Wide WebPolitical scienceHistoryLawMathematicsGeographyPhilosophyAlgorithm

Abstract

fetched live from OpenAlex

Abstract Over the past decade, Islamic State (ISIS) has made numerous attempts to propagate their beliefs on a global scale via a range of social media platforms (e.g. Twitter ), enabling them to reach an extensive audience within a very short time span; when successful, people enlist as supporters of their ideas and, essentially, become radicalised. ISIS also achieve this through publishing propaganda materials, such as the two online magazines Dabiq and Rumiyah ( Heidarysafa et al. 2019 ). In this paper, our focus lies with the former. Through a transitivity analysis of three issues from Dabiq , this paper explores how the in-group (the believers) and the Other (the non-believers) are represented in the magazine. The transitivity framework is useful here because it exposes the linguistic choices that people make and, in turn, reveals how they perceive their world. To retrieve both quantitative and qualitative findings, the UAM Corpus Tool ( O’Donnell 2016 ) is employed.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.789
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.206
Teacher spread0.192 · 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