Mainstreaming ecosystem services: The hard work of realigning biodiversity conservation
Bibliographic record
Abstract
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 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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.119 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".