Wildlife Conservation…in Afghanistan?
Bibliographic record
Abstract
Conservation projects multitask in conflict zones, blending development and conservation goals. The reaction we get whenever we speak about our conservation work in Afghanistan is the same: “Wildlife conservation in Afghanistan?” At first, the idea strikes people as strange, perhaps even preposterous. The common perception of Afghanistan is that of a depauperate landscape, largely devoid of wildlife worth saving. And given the many challenges facing Afghanistan, isn't conservation a distraction from the main mission of stabilizing the country, anyway? Surprisingly enough, Afghanistan has a serse array of species, in unique combinations, because of its place in the world. Moreover, instead of being a luxury, biodiversity conservation can contribute to the mission of stabilization. In fact, conservation projects in the Congo, Southern Sudan, and many other conflict zones are being used to develop civil society and sustainable economic opportunities. Afghanistan is but one example of this trend. As a waypoint on the Silk Road, Afghanistan has a long history as a cultural crossroads between East and West. It has an even longer history as a biological crossroads. Afghanistan lies at the intersection of three of the world's biogeographic realms: the Palearctic, Indomalayan, and Afrotropic bioregions. Tigers from East Asia and cheetahs from Africa were until recently part of Afghanistan's biota. Although they disappeared from Afghanistan in the twentieth century, nine other species of cat remain—more than twice the number of those in the United States and Canada combined, in an area about one twenty-fifth the size.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".