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Record W2109351681 · doi:10.21427/d75b6g

Digital technologies and the future of radio: lessons from the Canadian experience

2006· article· en· W2109351681 on OpenAlexaboutno aff
Brian O’Neill

Bibliographic record

VenueArrow - TU Dublin (Technological University Dublin) · 2006
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsTelecommunicationsDigital radioPaceGovernment (linguistics)BusinessRadio broadcastingPolitical scienceEngineeringGeography

Abstract

fetched live from OpenAlex

This paper examines the position of digital radio in Canada. It examines the Canadian experience of digital radio development from its introduction in 1995 to the present and asks whether the approach adopted and the lessons learned provide useful models for application elsewhere. Three main strands form the background to digital radio’s current stage of development: firstly, the introduction and early support for Digital Audio Broadcasting or (DAB) in the mid 1990s; secondly, the response of the radio industry to the internet and new media as complementary to traditional radio broadcasting provision; and thirdly, the more recent experience of the introduction of satellite radio in Canada. The focus for this particular paper’s analysis is the revised digital radio policy issued by the Canadian Radio-Television and Telecommunications Commission (CRTC) in December 2006, replacing the earlier transitional digital radio policy of 1995, and seeking to implement a multi-platform framework in an increasingly complex technological environment. The paper assesses initial response to the new digital radio policy and examines some of the potential scenarios for the future environment of radio. The research is informed by policy analysis, interviews and expert opinions with leading members of the Canadian broadcasting profession.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.183
Teacher spread0.175 · 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 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

Citations1
Published2006
Admission routes1
Has abstractyes

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