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Record W2254746116 · doi:10.24135/pjr.v19i1.236

Press freedom and communication rights: What kind of journalism does democracy need?

2013· article· en· W2254746116 on OpenAlexaff
Robert A. Hackett

Bibliographic record

VenuePacific Journalism Review – Te Koakoa · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPublic sphereDemocracyFreedom of the pressJournalismLiberalismElitismSociologyCivil societyLiberal democracyPolitical scienceLawDeliberative democracyPolitical economyLaw and economicsPolitics

Abstract

fetched live from OpenAlex

The task of identifying appropriate models of journalism for Pacific Island nations as they strive for more democratic governance is not a straightforward one. This article summarises several contending models of democracy—market liberalism/competitive elitism, public sphere liberalism, and radical democracy—and their attendant expectations of news media. When measured against the stated ideals of press freedom, and notwithstanding the emergence of the internet, the existing news systems of the dominant Western liberal-democracies, notably the US and UK, have significant democratic shortcomings, in relation to ‘watchdog’, public sphere, community-building and communicative equality criteria. Accordingly, the author argues that the practices and concept of press freedom need to be expanded and supplemented by a broader understanding and implementation of communication rights, entailing legal and cultural forms that support the full participation of all segments of society in the social cycle of communication. Such a paradigm is especially appropriate for postcolonial countries dealing with issues of economic development and inter-ethnic conflict.

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)0.018
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.037
Scholarly communication0.0180.028
Open science0.0010.006
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0080.002

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 head0.017
GPT teacher head0.281
Teacher spread0.264 · 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 designTheoretical or conceptual
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

Citations13
Published2013
Admission routes1
Has abstractyes

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Same venuePacific Journalism Review – Te KoakoaSame topicIsland Studies and Pacific AffairsFrench-language works237,207