Visioning African lionscapes: Securing space, mobilizing capital, and fostering subjects
Bibliographic record
Abstract
In September 2016, 14 months after the illegal killing of Cecil the lion raised an international furore over trophy hunting, 58 individuals gathered at Oxford University for the Cecil Summit, a meeting of experts designed to vision the future of lion conservation in honor of Cecil. This paper explores the Cecil Summit through an analytic of government as a means to provide new insights into securitized and neoliberal conservation governance in action. On this basis, we show how the actors emboldened by the Cecil Moment claimed the authority to vision the Cecil Movement. Using video and document review, and semi-structured interviews, our discourse analysis highlights three components of intervention into African lionscapes emerging from the summit—securing space, mobilizing capital, and producing subjects—that are founded upon claims to scientific and economic rationality as well as specific representations of lions and rural Africans. Our analysis of the vision contributes to recent discussions in political ecology about the dovetailing of conservation, security, the economy—and we add—subjectivity. We conclude by pointing to the way in which militarized conservation appears to be inching closer to the lion and offering a critique of the vision for lion conservation put forward at the Cecil Summit.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.035 |
| Scholarly communication | 0.006 | 0.007 |
| 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".