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Record W4281757060 · doi:10.1109/tbc.2022.3163818

IEEE Transactions on Broadcasting Special Issue on: 5G Media Production, Contribution, and Distribution

2022· article· en· W4281757060 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Broadcasting · 2022
Typearticle
Languageen
FieldEngineering
TopicTelecommunications and Broadcasting Technologies
Canadian institutionsCommunications Research Centre Canada
FundersInsight SFI Research Centre for Data AnalyticsScience Foundation IrelandEuropean Commission
KeywordsBroadcasting (networking)The InternetProduction (economics)Computer scienceTelecommunicationsConsumption (sociology)Service providerService (business)MultimediaBusinessWorld Wide WebComputer networkMarketing

Abstract

fetched live from OpenAlex

The media landscape is undergoing unprecedented transformations. Content creators and service providers had been for decades in control of how technology tailored to media applications was deployed and adopted in networks and devices to serve audiences. Disruption started when the Internet and global technologies and networks enabled content delivery and consumption at anytime, anywhere, and by means of any device. This new paradigm, where the Internet is fully accessible to the media industry, creates opportunities for innovation across the entire value chain, involving new services, innovative products, and a direct relationship with users.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.753
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.219
Teacher spread0.204 · 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