Madagascar's Burning Issue: The Persistent Conflict over Fire
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsChristian A. KullChristian A. Kull is an assistant professor with the Department of Geography at McGill University in Montreal, Quebec. His research interests include the politics of resource conservation, political ecology, wildland fire, and the social aspects of environmental transformations, particularly in Madagascar, where he has spent 24 months since 1992. This research was made possible by the good will of the tantsaha the author encountered in field sites, as well as, in particular, by Nancy Peluso and her colleagues at the University of California at Berkeley, Joelisoa Ratsirarson at the University of Antananarivo, and numerous researchers, foresters, conservationists, and friends in Madagascar and elsewhere. The research upon which this article is based was funded by the U.S. National Science Foundation (SBR 9811046), a U.S. Environmental Protection Agency graduate fellowship, and the University of California at Berkeley. Kull can be reached at christian.kull@alum.dartmouth.org.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it