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Introduction

2020· book-chapter· en· W4233206023 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOxford University Press eBooks · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicFreedom of Expression and Defamation
Canadian institutionsnot available
Fundersnot available
KeywordsRelation (database)Economic JusticeValue (mathematics)Political scienceSociologyLawPublic relationsMedia studiesComputer science

Abstract

fetched live from OpenAlex

This chapter sets up the thesis of the book: Crime coverage practices serve as a lens to consider underlying cultural attitudes to concepts like privacy, public, public right to know, and justice. Differing decisions, for example, about whether to name suspects, suggest varying beliefs about the value of privacy and the public right to know. The chapter outlines the methodology and situates the work in relation to Daniel Hallin and Paulo Mancini, whose book <italic>Comparing Media Practices</italic> influenced the selection of countries, as well as the initial premises. We name the ten countries that comprise the basis of our comparison, and briefly introduce our three media models: the Protectors (Germany, the Netherlands, Sweden), the Watchdogs (the United Kingdom, Ireland, Canada, and the United States), and the Ambivalents (Spain, Italy, and Portugal). The chapter concludes with a brief overview of individual book chapters.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.875
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.030
GPT teacher head0.228
Teacher spread0.198 · 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