Batwa Indigenous Peoples forced eviction for “Conservation”: A qualitative examination on community impacts
Bibliographic record
Abstract
In 1991, the Ugandan government formally established National Parks within the ancestral homelands of the Batwa Peoples. No consultation was carried out with local Batwa communities, and they were consequently forcibly evicted from their Forest home. With this, we sought to better understand the impacts of forced Land eviction through the lens of solastalgia. Nineteen semi-structured interviews were carried out with adult Batwa Peoples of varying age and gender in Uganda from August to November 2022. Interviews were transcribed verbatim, and thematic analysis was carried out on the interview transcripts to identify themes from the initial codes. Four overarching themes were identified, including: 1) Our love and connection with the Forest; 2) What was left in the Forest when we were evicted; 3) What eviction from the Forest did to us as Batwa Peoples; and 4) Batwa People's Landback and returning to the Forest ('Indigenous Lands back into Indigenous hands'). As movement towards the global "30 by 30" conservation agenda occurs, we urge researchers, policy makers, and leaders to listen to the voices of Indigenous Peoples like the Batwa with a key focus on Landback and movement towards a clearer understanding and appreciation of the impacts of Western conservation agendas on Indigenous Peoples globally.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".