Rethinking strategy in environmental governance
Bibliographic record
Abstract
This paper presents a novel framework for analyzing the formation and effects of strategies in environmental governance. It combines elements of management studies, strategy as practice thinking, social systems theory and evolutionary governance theory. It starts from the notion that governance and its constitutive elements are constantly evolving and that the formation of strategies and the effect strategies produce should be understood as elements of these ongoing dynamics. Strategy is analyzed in its institutional and narrative dimensions. The concept of reality effects is introduced to grasp the various ways in which discursive and material changes can be linked to strategy and to show that the identification of strategies can result from prior intention as well as a posteriori ascription. The observation of reality effects can enhance reality effects, and so does the observation of strategy. Different modes and levels of observation bring in different strategic potentialities: observation of self, of the governance context, and of the external environment. The paper synthesizes these ideas into a framework that conceptualizes strategies as productive fictions that require constant adaptation. They never entirely work out as expected or hoped for, yet these productive fictions are necessary and effective parts of planning and steering efforts.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.037 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".