Fields of green: Corporate sustainability and the production of economistic environmental governance
Bibliographic record
Abstract
This article critically examines the production of economistic fields of environmental governance in the context of global summits like Rio + 20. It focuses on the constitutive work performed by diverse actors in extending corporate sustainability logics, social technologies, and organizational forms initially enacted at the 2012 Corporate Sustainability Forum (CSF). Fields are defined as dynamic, relational arenas featuring particular logics, dynamic actor positions, and organizational forms. Corporate sustainability exemplifies how the language and practices of economics have reshaped approaches to environmental protection and sustainable development. Although numerous studies have looked at the implementation of market-oriented approaches, less attention has been focused on the constitutive processes that animate and expand economistic fields of governance over time. Our analysis emphasizes diffuse processes of economization as central to the reproduction and extension of fields. The article addresses three key issues: (1) how global corporate sustainability networks help to constitute economistic fields of governance, (2) the extent to which major events contribute to field configuration, and (3) the processes through which field elements—logics, social technologies, and organizational forms—transpose onto related fields of governance. Field configuration produces economistic environmental governance by solidifying business logics, enabling new actor-networks, launching new global-scale initiatives, and enhancing the role of UN agencies in promoting corporate sustainability. We illustrate field configuration with two examples: the Natural Capital Declaration and the Green Industry Platform. Our analysis highlights the diffuse power of field dynamics in which discursive and social entanglement and transposition reproduce and extend corporate sustainability beyond current institutional boundaries.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.058 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".