“Removing an Ogiek from the Forest is like removing a fish from water”: A qualitative examination on Ogiek community impacts from forced land eviction for conservation
Bibliographic record
Abstract
Despite evidence that Indigenous Peoples are better guardians of their Forests than international or state protection agencies, they continue to be forcibly evicted from their Lands. Additionally, despite the known impacts that forced land eviction for conservation has on the well-being of Indigenous Peoples in varied contexts, there remains limited appreciation of the well-being impacts of forced land eviction within Kenya from an Indigenous perspective. With this, the aim of this research was to better understand the well-being impacts of forced land evictions from an Ogiek perspective. Twenty-six semi-structured interviews and one sharing circle (n = 7) were carried out with Ogiek Peoples in Kenya between December 2021 and March 2023. The interviews and sharing circle were transcribed verbatim, then reflexive thematic analysis was carried out through iterative coding to identify key themes. Six themes were characterized including: 1) Our cultural practices, ceremonies, and spirituality are tied to our identity as Ogiek; 2) Our foods and plants are our medicines; 3) Maintaining our culture in an everchanging world; 4) The Forest and Ogiek are as one in a reciprocal relationship; 5) Removing an Ogiek from the Forest is like removing a fish from water; and 6) Hope that our rights will be recognized. Findings also demonstrated that the forced displacement of Ogiek Peoples by government entities has continued to impact the social and economic vitality of their communities. Our study exemplifies the substantial and ongoing impacts of colonial conservation approaches on Ogiek Peoples in Kenya, and highlights the continued need for local and international allies to stand in solidarity with and support Ogiek and other Indigenous Peoples in their efforts to return as the original stewards of their Forest and other homelands.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".