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Record W2109710977 · doi:10.24135/pjr.v11i2.1053

Different strokes for different folk: Regulatory distinctions in New Zealand media

2005· article· en· W2109710977 on OpenAlexaboutno aff
Gavin Ellis

Bibliographic record

VenuePacific Journalism Review – Te Koakoa · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsStatutory lawProject commissioningArgument (complex analysis)Government (linguistics)State (computer science)Divergence (linguistics)Regulatory statePublishingLegislatureScarcityRegulatory authorityBroadcasting (networking)Public relationsSociologyLawPolitical economyEconomicsPolitical scienceMarket economyPublic administration

Abstract

fetched live from OpenAlex

For much of the past century there was broad acceptance of the stark contrast between the state’s involvement in the regulation of the content of broadcasting and its laissez-faire relationship with the columns of the press. The ‘failed market’ argument that substantiated regulation of the airwaves was difficult to counter. Fundamental changes in technology and media markets have, however, rendered the rationale open to challenge. Some aspects of the ‘failed market’, such as frequency scarcity, simply do not apply in the digital age. This article examines the nature of media regulation in New Zealand, noting its similarity to the dichotomous approach in Britain, Canada and Australia but also its divergence toward a more neoliberal market model that largely limits statutory oversight to matters that fall broadly into the categories of morals and ethics. It argues that, given the New Zealand government’s decision more than 15 years ago to forego regulation of ownership or the mechanisms that would serve the public good aspirations of a Reithian model, the continuing role of the state in regulation of broadcasting is questionable. A replacement model could be based on an effective regulatory body already present in the New Zealand media industry—the Advertising Standards Authority

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.762
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.043
GPT teacher head0.322
Teacher spread0.280 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2005
Admission routes1
Has abstractyes

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