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Record W6922343317 · doi:10.11575/prism/38289

Impact and Utility of the Alberta Narratives Project

2020· other· en· W6922343317 on OpenAlexaboutno aff

Bibliographic record

VenuePRISM (University of Calgary) · 2020
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeWork (physics)Climate changeEnergy (signal processing)Public policyEnvironmental policy

Abstract

fetched live from OpenAlex

Alberta’s economy heavily relies on the traditional extraction of fossil fuels, which makes discussing their role in anthropogenic climate change intensely polarizing. The Alberta Narratives Project (ANP) represents the work of Alberta environmental non-government organizations to create a narrative connection between the general public, science, and policy makers. My Capstone aims to answer how effective the language and delivery of the ANP were at creating positive discussions surrounding energy, behaviour change (on the individual level), and overall environmental impact. Interviews provided the basis for an online survey of 365 individuals. Participants found their communications capacities increased and felt confident talking about climate change and energy in Alberta. However, participants also felt unsupported by their employers and facilitators, particularly with respect to follow-up. The ANP created more confident, informed climate science communicators close to the project, but to reach the public with the ANP, ENGOs need to offer more long-term support.

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)0.008
metaresearch head score (Gemma)0.013
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.427
Threshold uncertainty score0.860

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.003
Scholarly communication0.0080.002
Open science0.0020.008
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0430.006

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 head0.229
Teacher spread0.214 · 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
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
Published2020
Admission routes1
Has abstractyes

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