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Record W4281769732 · doi:10.1177/25148486221105153

Mainstreaming ecosystem services: The hard work of realigning biodiversity conservation

2022· article· en· W4281769732 on OpenAlexfundno aff
Daniel Chiu Suarez

Bibliographic record

VenueEnvironment and Planning E Nature and Space · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNational Science Foundation
KeywordsEcosystem servicesMainstreamingMainstreamWork (physics)NegotiationPoliticsNatural capitalEnvironmental resource managementPolitical scienceSociologyPublic relationsBusinessEcosystemEcologyEconomicsSocial scienceEngineering

Abstract

fetched live from OpenAlex

For over two decades, proponents of “ecosystem services” approaches have endeavored to transform the field of biodiversity conservation. In this article, I examine the work of the Natural Capital Project to show how the “mainstreaming” of ecosystem services has required not just hard work but specific forms of work performed by specific types of actors with specific sets of capabilities working through characteristic sorts of organizational contexts. I draw on key theorizations from organization studies to interpret the politics of ecosystem services and conceptualize the conditions (fragmented fields), practices (bricolage), actors (institutional entrepreneurs), and power relations (hegemonic) which have together comprised this work and underpinned ongoing efforts to realign the organizational forms and functions of mainstream conservation. I emphasize how tracing these micro-social foundations—the embedded agencies of those using ecosystem services to contextually negotiate real-world conservation interventions—is crucial to understanding the dynamics of broader and increasingly pronounced macro-institutional shifts in conservation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.557

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 head0.008
GPT teacher head0.170
Teacher spread0.163 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2022
Admission routes1
Has abstractyes

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