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Record W2896008304 · doi:10.1177/1329878x18803730

Understanding the ways missing and murdered Indigenous women are framed and handled by social media users

2018· article· en· W2896008304 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMedia International Australia · 2018
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsLaurentian University
Fundersnot available
KeywordsIndigenousCognitive reframingGender studiesIdeologySociologySocial mediaPublishingProject commissioningPolitical scienceMedia studiesCriminologySocial psychologyPsychologyPoliticsLaw

Abstract

fetched live from OpenAlex

The media plays a large role in facilitating negative racial and gender ideologies about Indigenous women. In Canada, as we struggle with the national crisis of missing and murdered Indigenous women (MMIW), researchers have collected data from social media (SM) and identified that subversive texts about Indigenous women perpetuate a racialized violent discourse. Given that many Indigenous peoples, including Indigenous youth, have smart phones and/or other ways to access SM they too are exposed to the discourse that subjugates, vilifies and dehumanizes Indigenous women, many of whom are family or community members. Our research investigates the messages shared on #MMIW and identifies a reframing by hashtag users. The results assist in understanding how SM plays a role in perpetuating stereotypes about Indigenous peoples but also how SM can be used to mitigate those messages.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.171
GPT teacher head0.367
Teacher spread0.196 · 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