Continuity and change within the social-ecological and political landscape of the Maasai Mara, Kenya
Bibliographic record
Abstract
Traditional livestock management has historically been blamed for the mismanagement of rangelands, but there is a growing recognition of the importance of extensive herding strategies and the local knowledge embedded in these practices. Here, we apply the lens of continuity and change to understand how local herders interpret environmental change. By exploring traditional rangeland indicators as used by Maasai herders, we highlight some of the forces of change that appear to constrain the application of local knowledge of rangeland health. Fieldwork was conducted from January to August 2013 in the Mara Division, Narok County, Kenya, employing semi-structured interviews, transect walks, focus groups, participatory mapping and participant observation. Findings suggest that continuity exists in many of the traditional methods of observing land and livestock. However, various obstructions are surfacing in a political landscape in which local knowledge holders are not always able to put their knowledge and observations into practice. These obstructions of knowledge, practices and skills occurred through three broad forces involving acculturation, prohibition and applicability. As possible consequences of a system in transition, these forces illustrate the unbalanced nature of overlap between heterogeneous users, conflicting interests and power differentials. In order to facilitate continued importance and growth of local knowledge, we conclude that resource and protected area managers must recognize local knowledge holders and ensure such knowledge is considered as more than anecdotal or strategic. By encouraging hybrid knowledge co-production in management decisions, the decision-making frame can be broadened to include herders for more inclusive decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".