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Record W4388908098 · doi:10.53288/0404.1.06

Respiration

2023· book-chapter· en· W4388908098 on OpenAlexaff
Ayesha Vemuri, Hannah Tollefson

Bibliographic record

VenuePunctum Books · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsMcGill University
FundersRice University
KeywordsIndigenousRespirationEcosystemCapitalismEcologyHistoryAstrobiologyBiologyPolitical scienceBotanyPolitics

Abstract

fetched live from OpenAlex

This chapter thinks with respiration as an elemental process of chemical exchange engendered by solarity in forest ecosystems. While photosynthesis converts sunlight into usable energy through carbon capture, respiration is the process of release and decay that makes these ecosystems sites of symbiosis. Attuned to solarity, we follow stories of how such energy conditions and moves through various forms of life and death in our heliocentric universe. Learning from the unstable and mutually beneficial relationships that characterize actually existing forests, we scrutinize the chemical, social, and analogical meanings of solar-inflected respiration. As key mediators of sunlight and carbon, trees are often valued for the services they provide as so-called “planetary lungs” and carbon sinks in times of climate crisis. Interrogating the promises and perils of such utilitarian conceptualizations of the natural world, we consider the ways in which these co-called lungs are unevenly valued and cared for. With compromised conditions of livability across more-than-human social worlds shaped by colonial capitalism and ongoing histories of imperialism, we look to the ways that Indigenous, Black and other marginalized knowledge keepers engage with the notion of respiration in seeking a more just distribution of planetary breathability.

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)0.000
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.121
Threshold uncertainty score0.403

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.1210.069

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 head0.039
GPT teacher head0.267
Teacher spread0.227 · 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
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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