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Record W4367678001 · doi:10.1108/dpm-11-2022-0222

The blame game: disaster, queerness and prejudice

2023· article· en· W4367678001 on OpenAlexaff
Ashleigh Rushton, Jazmin Scarlett

Bibliographic record

VenueDisaster Prevention and Management An International Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsUniversity of the Fraser Valley
Fundersnot available
KeywordsBlameQueerHarmPrejudice (legal term)Sexual orientationValue (mathematics)NarrativeSociologySocial psychologyGender studiesCriminologyPolitical sciencePublic relationsPsychology

Abstract

fetched live from OpenAlex

Purpose The purpose of this article is to draw attention to how harmful and inaccurate discourses pertaining to disaster responsibility is produced, the negative implications such narratives pose and the role of the media in the ways in which discourses about queerness and disaster are reported. Design/methodology/approach Throughout this paper, the authors detail examples of media reporting on discourses relating to people with diverse sexual orientation, gender identity, gender expression and sex characteristics (SOGIESC) being blamed and held responsible for disasters across the world. The authors examine the value of such reporting as well as describing the harm blame narratives have on queer people and communities. Findings There is little value in reporting on accounts of people publicly declaring that people with diverse SOGIESC are to blame for disaster. More sensitivity is needed around publishing on blame discourses pertaining to already marginalised communities. Originality/value This article contributes to the developing scholarship on lesbian, gay, bisexual, transgender, queer, intersex, agender, asexual and aromantic individuals, plus other gender identities and sexual orientations (LGBTQIA+/SOGIESC) and disasters by detailing the harm of blame discourses as well as drawing attention to how the media have a role to play in averting from unintentionally providing a platform for hate speech and ultimately enhancing prejudice against people with diverse SOGIESC.

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 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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.021
GPT teacher head0.357
Teacher spread0.336 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations7
Published2023
Admission routes1
Has abstractyes

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