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Record W4411602988 · doi:10.5040/9798881817213

Cultural Work of Community Radio

2019· book· en· W4411602988 on OpenAlexaboutno aff
Katie Moylan

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicRadio, Podcasts, and Digital Media
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Community radioSociologyMedia studiesEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Community radio is an established and key site for negotiations of social and political issues for marginalised communities. Given its inherently local nature (both geographically and ideologically), community radio is perfectly placed as a site for articulating community concerns. At the same time, given this local quality, the diverse ways in which stations—and broadcasters—negotiate their community concerns vary substantially from city to city and region to region across Canada and the US. The Cultural Work of Community Radio investigates the multiple modes of community and broadcasting practice at selected community stations, explores how these draw from and reflect ongoing concerns of their host city or region, and examines how on the ground practice maps on to overarching broadcast policy directives and guidelines. Focusing on community production practices with reference to policy frameworks around community representation, this book examines and compares differences in community radio production practices in Miami, Montreal, New Orleans, Toronto and tribal lands in Arizona.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.014
Scholarly communication0.0080.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.002

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 head0.066
GPT teacher head0.316
Teacher spread0.250 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2019
Admission routes1
Has abstractyes

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