<b>LONG BITTER ENEMIES NO MORE: IENGO CORPORATE PARTNERSHIPS AS A NEW PATHWAY TO INFLUENCE IN GLOBAL ENVIRONMENTAL GOVERNANCE</b>
Bibliographic record
Abstract
This dissertation examines how partnerships between leading environmental non-governmental organizations (IENGOs) and corporations shape the agency of nonstate actors in global politics. It contributes to the growing scholarship on non-substantialist approaches to the concept of agency in International Relations (IR) by analyzing how interactions between nonstate actors can influence their ability to act and exert influence in global politics. The central concept of agency reconfiguration is introduced. This concept argues that IENGO-corporate partnerships can create opportunities for nonstate actors to gain new capacities and influence in global politics. However, it also acknowledges potential trade-offs associated with such partnerships. To explore this concept, the dissertation first maps the landscape of IENGO-corporate partnerships. This includes a comprehensive list of corporate partners for four leading IENGOs (Greenpeace, EDF, FoE, and WWF), how these partnerships have evolved over time, and a typology of partnership structures. Finally, a process tracing approach is used to examine a specific case: the partnership between Greenpeace and Foron, a former German appliance manufacturer. Within-case evidence is used to link the events from the formation of the partnership to Greenpeace's agency reconfiguration, which ultimately positioned Greenpeace as a central actor in ozone governance, particularly the implementation of the Montreal Protocol.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".