Using scenario planning for stakeholder engagement in livelihood futures in the Great Limpopo Transfrontier Conservation Area
Bibliographic record
Abstract
Scenario planning has gained prominence among conservationists and policy-makers as a tool for planning, forecasting and learning about the future. This paper explores how participatory scenario planning was applied as a tool for promoting stakeholder engagement on discussions of desired livelihood futures. The study was conducted in Sengwe Communal lands, an area that falls within the Great Limpopo Transfrontier Conservation Area (GLTFCA). Data collection was based on semi-structured interviews, document reviews, focus group discussions and scenario workshops. Future desirable livelihoods that emerged include tourism enterprise development, small-scale irrigation, wildlife and livestock improvement, and energy generation. Development options imagined by locals are inseparable from contemporary politics of transfrontier conservation area governance requiring researchers to shift roles from being catalysts and knowledge brokers to facilitators of learning and negotiation. This paper contributes to contemporary debates on novel approaches to promote engagement with communities for improving biodiversity conservation and livelihoods in emerging transfrontier conservation areas.
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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 | 0.048 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".