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Record W3083125459 · doi:10.22215/etd/2015-11143

Uncertainty in Climate Change Discourse: An Examination of Regional Canadian Newspapers

2015· dissertation· en· W3083125459 on OpenAlexaffabout
Allison Neil

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsCarleton University
Fundersnot available
KeywordsFraming (construction)Climate changeNewspaperPolitical sciencePoliticsContext (archaeology)GeographyClimatologyLaw

Abstract

fetched live from OpenAlex

This thesis examines the prevalence and framing of climate change across seven regional Canadian newspapers, with a focus on climate uncertainty, from April 1 to September 30, 2013.Results indicate that climate change and uncertainty are prevalent across Canadian newspapers, and that there is a prevailing discourse.Climate change is primarily framed as a contemporary issue that exists on an international scale.It is often linked to environmental, political, energy, and scientific issues; however, climate change itself is primarily de-contextualized.Climate solutions are especially lacking from the discourse and climate responsibility is either non-existent or related to government.It is thus not surprising that uncertainty within the discourse is primarily about climate action and solutions.Although there are some statistically significant differences between the regional newspapers in regards to spatial framing, climate context, and climate attitude, there does not appear to be any underling spatial pattern therein.v IV. METHODOLOGY………………………………………………………………………………….4.1 I.

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)0.007
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.526

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.032
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0130.025
Science and technology studies0.0170.006
Scholarly communication0.0100.003
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.477
GPT teacher head0.492
Teacher spread0.015 · 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 designQualitative
Domainnot available
GenreEmpirical

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
Published2015
Admission routes2
Has abstractyes

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