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Record W4382680232 · doi:10.1075/jlp.22125.vau

ICT environmentalism and the sustainability game

2023· article· en· W4382680232 on OpenAlexaff
Hunter Vaughan, Anne Pasek, Nicholas R. Silcox, Nicole Starosielski

Bibliographic record

VenueJournal of Language and Politics · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsTrent University
Fundersnot available
KeywordsSustainabilityConceptualizationRhetoricInformation and Communications TechnologyRhetorical questionPoliticsPublic relationsEnvironmentalismPolitical scienceSociologyEcologyLaw

Abstract

fetched live from OpenAlex

Abstract Over the past three decades, corporate branding has trended strongly towards environmental conscientiousness and green rhetoric, often heralded under the term “sustainability” – a broad and mutable rhetorical strategy that not only serves industry self-interest but is mobilized by civil society actors as well. This tension is especially apparent in the information communication technologies (ICT) sector. Employing Wittgenstein’s concept of the language-game, this article describes how sustainability has been deployed by tech companies, and how these efforts have also been contested – and strategically mobilized – by activist environmental non-profits and critical scholars seeking to reform tech sector practices. Combining environmental communication, political economy, and discourse analysis, we investigate the conceptualization and communication of sustainability as a discourse within and against the sector.

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)0.004
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.028
Scholarly communication0.0080.007
Open science0.0010.005
Research integrity0.0020.002
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 head0.142
GPT teacher head0.430
Teacher spread0.288 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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