Dissolving the pastoral commons, enhancing enclosures: commercialization, corruption and colonial continuities amongst Maasai pastoralists of Southern Kenya
Bibliographic record
Abstract
AbstractMaasai pastoral landholdings presently collectively held and managed under group ranch tenure arrangement are increasingly under pressure to subdivide and privatize. Subsequent processes of defining, administering, allocating and securing land rights and associated resources within pastoral landholdings has remained largely contentious. The complex interplay between market forces, state bureaucracy (policy, legal and administrative framework), customary value systems and institutions in the process of allocating land rights against a backdrop of competing land-use options and mounting population pressure provides the setting for unpacking the dynamics of land related graft.This thesis presents new data to analyze emerging and increasing incidences of practices and activities that could generally be described as 'corrupt' in the process of subdivision and privatization of pastoral commons. Understanding the roles, interests and strategies of different social actors and institutions – local group ranch members, group ranch officials, ministry of lands officials, private sector investors (conservationists, tourism sector players, land surveyors, lawyers) - during the land subdivision processes, seen in the light of historical and current social, economic, and political trajectories, can help deepen our understanding of land related corruption and its likely impact on future land use trends and local livelihoods. In particular, community conservation initiatives driven by private sector investors, local community members' unfamiliarity with functioning of the state bureaucracy and personal agency in rent seeking tendencies inevitably have the strongest influence on social equity with respect to land and associated resources within the GR context.. However, the increase in land related graft is not a simple function of the shortfalls arising from market inequity and state inefficiency related outcomes. They are as much influenced by carry-over of practices of indigenous value systems on resource distribution based on reciprocity. These findings are relevant not only for Maji moto group ranch and group ranches adjacent to Maasai Mara game reserve, but also for pastoral livelihood and land-use options elsewhere in Kenya and sub-Saharan Africa
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".