Global hunting grounds: power, scale and ecology in the negotiation of conservation
Bibliographic record
Abstract
Increasingly, large international conservation organizations have come to rely upon market-oriented interventions, such as sport trophy hunting, to achieve multiple goals of biodiversity protection and ‘development’. Such initiatives apply an understanding of ‘nature’-defined through an emerging discourse of global ecology-to incorporate local ecologies within the material organizational sphere of capital and transnational institutions, generating new forms of governmentality at scales inaccessible to traditional means of discipline such as legislation and enforcement. In this paper, I historicize debates over ‘nature’ in a region of northern Pakistan, and demonstrate how local ecologies are becoming subject to transnational institutional agents through strategies similar to those used by colonial administrators to gain ecological control over their ‘dominions’. This contemporary reworking of a colonialist ethic of conservation relies rhetorically on a discourse of global ecology, and on ideological representations of a resident population as incapable environmental managers, to assert and implement an allegedly scientifically and ethically superior force better able to respond to assumed degradation. In undertaking such disciplinary projects, international conservation organizations rely on, and produce, a representation of ecological space as ‘global’ to facilitate the attainment of translocal political-ecological goals.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.058 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.008 |
| 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".