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Record W2554927990 · doi:10.1057/978-1-137-52321-1

Media and Global Climate Knowledge

2016· book· en· W2554927990 on OpenAlexaff
Risto Kunelius, Elisabeth Eide, Matthew Tegelberg, Dmitry Yagodin

Bibliographic record

VenuePalgrave Macmillan US eBooks · 2016
Typebook
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsYork University
Fundersnot available
KeywordsClimate changeClimatologyPolitical scienceGeographyRegional scienceEnvironmental scienceEnvironmental planningMeteorologyGeologyOceanography

Abstract

fetched live from OpenAlex

This book is a broad and detailed case study of how journalists in more than 20 countries worldwide covered the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment (AR5) reports on the s

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)0.000
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.062
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0020.003
Scholarly communication0.0100.009
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0620.013

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 head0.164
GPT teacher head0.381
Teacher spread0.217 · 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

Citations20
Published2016
Admission routes1
Has abstractyes

Explore more

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