Semi-structured interview framework from: Growing together: Strengthening the partnership and impact of a zoo and community-led conservation organization
Bibliographic record
Abstract
Indigenous peoples and local communities play a pivotal role in addressing the climate and biodiversity crises. The South Rift Association of Land Owners (SORALO) is a community-driven organization, representing 30 Indigenous Maasai communities in Kenya, which cultivates resources to support communal landscape management. The Cincinnati Zoo & Botanical Garden (CZBG) is an Association of Zoos and Aquariums (AZA)-accredited zoological facility and has partnered with SORALO since its inception in 2006. This study evaluates the strengths and challenges of the SORALO-CZBG partnership using the PARTNERS framework and examines its alignment with the Naivasha Vision—principles established by African conservation leaders in 2023 at the African Community Conservation Forum. We conducted eight semi-structured interviews with SORALO and CZBG staff (four from each organization) and analyzed them through thematic analysis and constant comparative methodology. The analysis shows the PARTNERS framework is an effective tool for evaluating the strengths and opportunities for growth between an international conservation partner and a community-based conservation organization. The SORALO-CZBG partnership is aligned with the Naivasha Vision and has strengths in all facets of the PARTNERS framework. Key opportunities for growth in the partnership were found in aptness, responsiveness, negotiation, empathy, and strategic support. This evaluation acted as a tool for SORALO-CZBG leaders to talk through strengths and opportunities for growth, along with plans to address each. Practical Implication: Our findings highlight key factors that strengthen conservation partnerships and offer recommendations for conservation organizations, especially international NGOs, to examine their partnerships and practices to enhance collaboration with community-led conservation initiatives and best practices written by community conservation leaders.
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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 | 0.027 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".