Review of Land, Investment & Politics: Reconfiguring East Africa’s Pastoral Drylands. Edited by Jeremy Lind, Doris Okenwa & Ian Scoones. Woodbridge, Suffolk: James Currey, an imprint of Boydell and Brewer, 2020.
Bibliographic record
Abstract
Book details Edited by Jeremy Lind, Doris Okenwa & Ian Scoones Land, Investment & Politics: Reconfiguring East Africa’s Pastoral Drylands. Woodbridge, Suffolk: James Currey, an imprint of Boydell and Brewer, 2020. 224 pages, ISBN 978-1-84701-252-4 (James Currey hardback) and ISBN 978-1-84701-249-4 (James Currey paper) Jeremy Lind, Research Fellow, Institute of Development Studies, University of Sussex. Co-editor of “Pastoralism and Development in Africa” (2013) Doris Okenwa, Ph.D student in Anthropology, London School of Economics. Ph.D research on oil discoveries in Turkana County, Kenya. Ian Scoones, Professorial Fellow, Institute of Development Studies, University of Sussex. Co-Director of ESRC STEPS (Social, Technological and Environmental Pathways to Sustainability) Centre, and leader of the European Research council project PASTRES (Pastoralism, Uncertainty and Resilience). Author of “Africa’s Land Rush: Rural Livelihoods and Agrarian Change”.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".