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Record W2788876259 · doi:10.1386/joacm_00036_1

Intersectionality in autonomous journalism practices

2018· article· en· W2788876259 on OpenAlexaff
Sandra Jeppesen

Bibliographic record

VenueJournal of Alternative & Community Media · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsLakehead University
Fundersnot available
KeywordsGrassrootsMainstreamSolidaritySociologyJournalismIntersectionalityOppressionAlternative mediaGender studiesAccountabilityMedia studiesPublic relationsPolitical sciencePoliticsLaw

Abstract

fetched live from OpenAlex

Media activists who are women, queer, trans*, Indigenous and/or people of colour are shifting mediascapes through intersectional autonomous journalism practices. This community-based co-research project analyses data from six semi-structured focus group workshops with media activists, who identify a contradictory logic between mainstream and alternative journalism. Two distinct autonomous journalism practices emerge that complement and extend traditional horizontal prefigurative media activist practices through an attentiveness to intersectional identities and interlocking systems of oppression. In rooted direct-action journalism, grassroots autonomous journalists collectively report from a perspective rooted in the concerns of the movement, creating media as a direct-action tactic; whereas in solidarity journalism, autonomous journalists report across movements in solidarity with intersectionally marginalised groups and communities. We argue that, emphasising intersectional mutual aid, relationship building, consent, accountability and content co-creation, these value-based practices have begun to shift dominant media and cultural logics. Finally, we offer critical reflections on some of the challenges inherent in these practices, including a meta-analysis of the intersectional value practices in our activist co-research methodology.

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)0.008
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.162
GPT teacher head0.431
Teacher spread0.269 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations8
Published2018
Admission routes1
Has abstractyes

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