The Community Land Act and the subdivision of Kenya’s Maasailand’s remaining commons: implications for community conservation
Bibliographic record
Abstract
The introduction of the Community Land Act (2016), heralded by Kenya’s National Land Policy under its 2010 Constitution, reignited debates around the formalisation of customary property rights, leading many Maasai group ranches to dissolve communal land into private, individualised parcels rather than register as community lands. This trend has often resulted in land enclosures and unsustainable resource use, threatening vital community-managed resources such as forests, grasslands, and wildlife. This study employs a qualitative comparative case study of two Maasai group ranches’ transition to private tenure in order to investigate local perceptions of the CLA and the factors motivating communities to move away from communal land holdings. It also examines how the two different approaches to land subdivision affect resource management and conservation outcomes. It draws from ethnographic fieldwork conducted in Oloirien (Narok County) and Olgulului/Ololarashi (Kajiado County), including semi-structured interviews, household questionnaires and participant observation, conducted between 2022 and 2023 among Maasai communities, as well as a review of secondary sources. The findings reveal that Olgulului/Ololarashi, which integrated demands for private property rights with communal access and management of the commons, was able to mitigate many unintended consequences of privatisation, such as path dependency and resource fragmentation. In contrast, Oloirien’s approach led to increased land enclosures and weakened collective management. This paper argues that, in an enclosure context, conservation initiatives that allow for the continuity of customary resource management and give people a tangible stake in projects are more likely to foster a collective sense of environmental responsibility and stewardship. These insights have broader relevance for land policy and conservation strategies across African rangelands.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".