A Critical Analysis of the Relationship Between Climate Change, Land Disputes, and the Patterns of Farmers/Herdsmen’s Conflicts in Nigeria
Bibliographic record
Abstract
Relying on the Nigeria Watch database and newspaper reports from August 2014 to April 2018, this study analyses the root causes, patterns, and politicisation of the farmers/herdsmen conflicts in Nigeria. This study critically examines the relationship between climate change, land disputes, and the patterns of farmers/ herdsmen conflicts in Nigeria. Scholars’ attempts to examine the relationship between environmental (in)sustainability and violent conflicts have been largely inconclusive. The recent conflicts between farmers and herdsmen may have taken a different pattern, especially in the North-Central region of Nigeria. Many people have attributed the increase in the conflicts between the two communities (farmers and herdsmen) to several non-environmental factors. The study adopts longitudinal research methods to unearth the connections between climate change, land disputes, and the patterns of the conflicts. It, however, looks at the conflict(s) as a product of environmental influences but escalated by the “vested interests” benefiting from the continued conflicts in the region.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".