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Record W3039720786

Rock 'n' Radio: When DJs and Rock Music Ruled the Airwaves

2017· book· en· W3039720786 on OpenAlexaboutno aff
Ian D. Howarth

Bibliographic record

Venuenot available
Typebook
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsRock musicArtGeologyMining engineeringVisual artsPopular music
DOInot available

Abstract

fetched live from OpenAlex

"Rock'n'Radio illustrates that Montreal was at the epicentre of the rock radio revolution in Canada, eventually attracting talented DJs from the U.S., Canada and the U.K. Their personal stories and the inevitable collision with the power of alternative FM rock radio in the late 60s take the reader through some of the best rock music recorded and the social changes that percolated in the background. The period 1926 to 1949 can be considered the Golden Age of radio when it was the heart of the North American family. Much to everyone's surprise, it survived the incursion of television to live another Golden Age, the 1960s and 1970s when rock 'n' roll music seeped its way onto mainstream radio, pushing aside Perry Como and the Dorsey Brothers for Elvis and The Beatles. The new golden era of radio spawned what would eventually be called Top 40 AM radio, whose premise was built on the philosophy: play all the hits, then play them again. Pioneer Top 40 DJs like Alan Freed in the U.S., widely recognized as the man who coined the phrase "rock 'n' roll," spawned a new breed of radio personalities, the fast-talking salesman who delivered the goods. Hundreds of radio stations in North American gave up their entire programming day over to rock music. And with that came a legion of young, hungry top 40 DJs such as Dave Boxer, Ralph Lockwood and Doug Pringle, looking for jobs at stations across Canada."--

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)0.003
metaresearch head score (Gemma)0.007
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.268
Threshold uncertainty score0.534

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0200.008
Scholarly communication0.0180.006
Open science0.0010.004
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0460.008

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 head0.036
GPT teacher head0.194
Teacher spread0.158 · 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
Published2017
Admission routes1
Has abstractyes

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