The Politics of Inclusive Development: Interrogating the Evidence
Bibliographic record
Abstract
It is now widely accepted that politics plays a significant role in shaping the possibilities for inclusive development. However, the specific ways in which this happens across different types and forms of development, and in different contexts, remains poorly understood. This collection provides the state of the art review regarding what is currently known about the politics of inclusive development. Leading academics offer systematic reviews of how politics shapes development across multiple dimensions, including through growth, natural resource governance, poverty reduction, service delivery, social protection, justice systems, the empowerment of marginalized groups, and the role of both traditional and non-traditional donors. The book not only provides a comprehensive update but also a groundbreaking range of new directions for thinking and acting around these issues. The book’s originality thus derives not only from the wide scope of its case-study material, but also from the new conceptual approaches it offers for thinking about the politics of inclusive development, and the innovative and practical suggestions for donors, policymakers, and practitioners that flow from this.
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 | 0.089 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.027 | 0.025 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".