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Record W4360611878 · doi:10.1080/09644016.2023.2193068

Spiral-scaling climate action: lessons from and for the academic flying less movement

2023· article· en· W4360611878 on OpenAlex
Ryan Katz-Rosene, Anne Pasek

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnvironmental Politics · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicConferences and Exhibitions Management
Canadian institutionsTrent UniversityUniversity of Ottawa
FundersCanada Research Chairs
KeywordsMovement (music)Action (physics)Spiral (railway)ScalingPolitical scienceGeodesyGeographyPhysicsGeometryEngineeringAestheticsArtMathematics

Abstract

fetched live from OpenAlex

The notion that scholars should reduce consumption of workrelated flight travel as a form of climate action has become common in academia. Proponents of this idea have coalesced into a sectoral movement seeking to have a more significant impact. This article critically reflects on the case of the Academic Flying Less Movement (AFLM) to conceptually explore how the environmental concerns of individual scholars might cohere and coalesce into something more powerful. We draw lessons from the AFLM’s existing efforts to change common academic practice through norm diffusion, while also interpreting lessons for the AFLM by developing a spiral model of strategic multi-scalar climate action, wherein the limitations of various modes of action compel scalar shifts towards different forms of action. Our analysis contributes to ongoing efforts in the field to develop more nuanced understandings of the value and limitations of small-scale, demandside actions within the broader constellation of climate action.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.150
GPT teacher head0.380
Teacher spread0.230 · 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