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Record W4391630709 · doi:10.1080/09505431.2024.2312703

Environmental governance through metrics: guest introduction

2024· article· en· W4391630709 on OpenAlexafffund
Allison Marie Loconto, Scott Prudham, Steven A. Wolf

Bibliographic record

VenueScience as Culture · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersMario Einaudi Center for International StudiesUniversity of Toronto
KeywordsCorporate governanceEnvironmental governancePolitical scienceBusinessEnvironmental planningPublic administrationEnvironmental resource managementGeographyEnvironmental science

Abstract

fetched live from OpenAlex

Metrics do not merely describe an external or objective reality but instead reflect and animate political struggles over what and how to govern. Examples include: Green House Gas inventories, the Extended Continental Shelf, and excess deaths attributable to COVID-19. Applied to environment, the desire to govern is at least as old as modernity, perhaps because we have never been able to fully realize the modern ideal of the dichotomous separation of nature-culture (Latour, 1993). We understand governing – and thus environmental governance – in the Foucauldian sense, i.e. the ensemble of ‘institutions, procedures, analyses and reflections, calculations and tactics that allow the exercise of a specific, albeit complex, form of power over a target population’ (Foucault, 2004, p. 111). In the twenty-first century, environmental governance is far more complex and polycentric than ever before. Another reason for the complexity of the ensemble of environmental governance is that it is effectively an agencement (Callon et al., 2013), composed of human and non-human actors who become the hybrid objects, subjects and authorities involved in the governing of relations that are sometimes incoherent and contradictory.In this special issue, we are concerned in particular with the problems posed by unsustainable interactions among the living and non-living elements of environments, which become the targets of varied forms of ‘ecogovernmentality’ (Luke, 1995). Fundamental to the forms of governmentality employed in the late 20th and early 21st centuries are efforts to both know the environment and its problems in order to manage them, but also the complex interactions between forms of knowing and ways of intervening and managing. STS scholars argue that data – i.e. temporally and/or spatially bounded representations of phenomena – is increasingly seen to be essential for managing environmental systems, practices and politics (Gabrys, 2016). This special issue pushes this argument further by focusing on the eventful lives of metrics that are used to bind knowing and managing into governing instruments (Lascoumes and Le Gales, 2007). Overall, the articles here discuss these questions: In representing environmental realities, how do metrics value some realities over others? How does such knowledge affect trust in the actors who govern the environment? How does such knowledge structure their accountability?

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.998
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.003
Scholarly communication0.0120.011
Open science0.0010.007
Research integrity0.0060.012
Insufficient payload (model declined to judge)0.0340.010

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 head0.007
GPT teacher head0.280
Teacher spread0.272 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations18
Published2024
Admission routes2
Has abstractyes

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