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Record W2535098520 · doi:10.1017/s0030605316000752

If biodiversity offsets are a dead end for conservation, what is the live wire? A response to Apostolopoulou & Adams

2016· article· en· W2535098520 on OpenAlexaff
Jessica Dempsey, Rosemary‐Claire Collard

Bibliographic record

VenueOryx · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsConcordia UniversityUniversity of British Columbia
Fundersnot available
KeywordsCapitalismCapitalist mode of productionMode of productionSurplus valueProfit (economics)CommodityBiodiversityEconomicsEconomyNeoclassical economicsMarket economyGeographyProduction (economics)EcologyPolitical scienceBiologyLaw

Abstract

fetched live from OpenAlex

Ecofeminist Maria Mies describes capitalist social relations as an iceberg. The visible tip represents the formal economy, where capitalist value emerges from exploited waged labourers and the circulation of monetized goods and assets. Underneath the waterline lurks the rest of the iceberg, and its size dwarfs the tip. Here, Mies points to a much larger world of exploitation on which commodity production and profit-making depend: women, colonies and, at the very base, nature. The bodies, places and materials of the submerged, invisible iceberg supply unwaged labour and unpriced inputs and energies that are productive; capitalism depends on this deeply undervalued work. Let us restate: capitalism exploits, yes, but strangely, it is a mode of organizing society that also relies on this exploitation. As Mies ([1986]1998, p. 200) writes, ‘the exploitation of colonies, as well as that of women and other non-wage workers, is absolutely crucial to the capitalist accumulation process’; this exploitation ‘constitutes the eternal basis for capitalist accumulation’ (Mies, 2007, p. 269).

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)0.020
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.021
Scholarly communication0.0100.027
Open science0.0030.006
Research integrity0.0200.033
Insufficient payload (model declined to judge)0.0100.002

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 head0.025
GPT teacher head0.274
Teacher spread0.249 · 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
GenreCommentary

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

Citations15
Published2016
Admission routes1
Has abstractyes

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