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Reading Technologies

2017· book-chapter· en· W4253009045 on OpenAlexaboutno aff

Bibliographic record

VenueThe MIT Press eBooks · 2017
Typebook-chapter
Languageen
FieldArts and Humanities
TopicLiterature, Film, and Journalism Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsIonogramOptimal distinctiveness theoryReading (process)GeographerTelecommunicationsRadio communicationsBroadcasting (networking)HistoryGeographyMedia studiesMeteorologyComputer scienceEngineeringSociologyPolitical scienceCartographyElectrical engineering

Abstract

fetched live from OpenAlex

This chapter examines the efforts to make the high-latitude ionogram legible, tracing the effects of that new legibility into wider, resonant views of the relationship between the North and communication failures. It first focuses on the transformations in the way the high-latitude ionogram was read. The same geophysical phenomena that disrupted Northern radio communications made high-latitude ionograms unreadable using standard techniques. Led by one of its founding members, Jack Meek, the Radio Physics Laboratory developed a set of reading regimes that would make these records readable for the first time. The second part of the chapter investigates how the connections built up through these techniques resonated far beyond the laboratory. By linking Northern geophysics and communications disruptions, the Laboratory furnished visual arguments for how defining elements of Canada’s northern-ness threatened reliable communications, feeding back into broader cultural narratives put forward by the Canadian Broadcasting Corporation and the geographer Louis-Edmond Hamelin.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.402
Threshold uncertainty score0.853

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0020.001
Scholarly communication0.0100.009
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.4020.309

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 head0.055
GPT teacher head0.236
Teacher spread0.181 · 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.

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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