Biodiversity in German development cooperation after the Kunming-Montreal Global Biodiversity Framework
Bibliographic record
Abstract
The biodiversity crisis of rapid species extinction is met with conservation measures on a global scale. The current thesis examines the case of German development cooperation as the biggest financier of biodiversity conservation in the Global South, focusing on the in-volvement of Indigenous Peoples and local communities (IP&LC) in the planning and imple-mentation of biodiversity projects in Bolivia, Colombia, Ecuador, and Peru. Based on inter-views with practitioners in German development cooperation and the protocol of a Bundes-tag hearing it is worked out how German conservation approaches often (still) follow a logic of ecological modernisation. On the other hand, the involvement of IP&LC is gaining sali-ence. By applying a post-colonial political ecology perspective, it is shown that on the imple-mentation level German development actors are aware of the importance of integrating the rights, worldviews and contributions of IP&LC into conservation. However, when it comes to policymaking, IP&LC are often still excluded due to structural obstacles. This bears the risk that IP&LC are merely included for the implementation of Western-designed conservation projects. Conversely, there is a chance that the growing awareness for IP&LC will translate into more participatory conservation that places human well-being at the centre.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".