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Record W7125009698 · doi:10.16995/regeneration.16556

Unalienating Carbon: Affect and Labour in Artisanal Carbon Removal Work

2025· article· en· W7125009698 on OpenAlexaff
Anne Pasek

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicEcocriticism and Environmental Literature
Canadian institutionsTrent University
Fundersnot available
KeywordsGreenhouse gasClimate changeCarbon sinkAgricultureWork (physics)Carbon fibersClimate change mitigationNarrative

Abstract

fetched live from OpenAlex

Carbon is rapidly undergoing a joint technological and cultural transformation. With the prolific rise of carbon dioxide removal in the climate pathways and net zero commitments that contour wider energy transitions, carbon emissions are increasingly positioned not just as future actions to be avoided, but as an already existing material sink newly opened to technical intervention and marketization. While climate models contemplate industrial-scale carbon removal technologies by the end of the century, carbon removal is currently characterized by small-scale, agricultural work: regenerative farming and biochar production. This article focuses on these forms of artisanal carbon removal, analyzing how its workers develop a unique experience of what is otherwise an invisible object of social anxiety: for them, carbon is an object of their labour, transformed through work into newly sensory and reparative forms. The narratives and affects of small-scale carbon removal work, accordingly, present a significant departure from the usual frames of climate and energy politics, offering rare possibilities for hope, regeneration, and relational capacities for direct and tangible action. Drawing on documentary media about and by regenerative agriculture and biochar practitioners, this article explores how such unalienated affects may culminate in new orientations for climate communication and politics.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.108
GPT teacher head0.454
Teacher spread0.346 · 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 teacher head, not a consensus.

Study designObservational
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

Citations1
Published2025
Admission routes1
Has abstractyes

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