Bibliographic record
Abstract
Conflicts over natural resources are common in developing countries, due to poorly defined property rights and limited state capacity for preventing conflict and because environmental incomes matter more to livelihoods than in rich countries. In Papua New Guinea (PNG), for example, the subject of the current study, almost one-quarter of households had land disputes in the previous 12 months, with disputes over agricultural and forestry resources, over development projects, and tribal fighting also frequently experienced. About seven percent of the land disputes and 40 percent of the tribal fights resulted in deaths. In this paper, geo referenced household survey data on disputes and conflicts, and remote sensing observations on forest losses in the local area over the prior ten years are used to show the frequency of conflict over natural resources, the distributional incidence of this conflict – whether rich or poor areas are more at risk – and the effect of large-scale environmental change, specifically deforestation, on the subsequent risk of conflict. A sharp increase in log exports, which saw PNG become the largest exporter to China as other countries withdrew from the tropical logs trade, represents an exogenous shock that helps to identify effects of deforestation on conflict rather than the reverse relationship.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".