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Record W4388192771 · doi:10.1177/1329878x231209599

First Nations media in the Closing the Gap era: navigating the new self-determination

2023· article· en· W4388192771 on OpenAlexaboutno aff
Archie Thomas, David Nolan, Kerry McCallum, Lisa Waller, Magali McDuffie

Bibliographic record

VenueMedia International Australia · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsnot available
FundersAustralian Research Council
KeywordsClosing (real estate)Government (linguistics)PoliticsState (computer science)Corporate governancePolitical sciencePolitical economyPublic relationsPublic administrationSociologyLawEconomicsManagementComputer science

Abstract

fetched live from OpenAlex

In 2020, a new Closing the Gap Agreement and an associated Joint Communications Strategy committed the Australian Government and state and territory governments to working with First Nations media to advance Closing the Gap aims, after lobbying by First Nations Media Australia. The new attention to First Nations media occurs after two decades of government disregard. We observe how First Nations media organisations have consistently advocated for a form of self-determination through First Nations-controlled communications, laying the groundwork for this shift. In doing so, they strategically adopt a political discourse to critique and promote reform of policy frameworks in their interests, highlighting tensions around the conceptualisation and practice of self-determination. We consider what may be required for a revised (re)adoption of self-determination as a policy to shift state-led governance, and to overcome the significant failures and limitations of policy processes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0120.032
Scholarly communication0.0150.019
Open science0.0010.015
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0050.001

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 head0.073
GPT teacher head0.363
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2023
Admission routes1
Has abstractyes

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