Strengthening conservation through localized definitions of wellbeing : understanding what is meant by 'A Good Life' in Namibia's Zambezi Region
Bibliographic record
Abstract
Effective conservation is more important now than ever before with biodiversity loss occurring at unprecedented rates. Conservation practices have evolved from ‘fence and fine’ strategies to participatory approaches. It is now widely accepted that conservation initiatives should deliver both socioeconomic benefits and biodiversity protection. One of the best known examples of achieving this is Namibia’s communal conservancy programme. This thesis sought to understand how communities in Namibia’s Zambezi Region define wellbeing in general and as a function of the conservancies. This work aimed to move beyond universal measures of socioeconomic wellbeing to a set that includes a broad suite of concerns that are economic and social, environmental, cultural and political. Findings are based on two months of fieldwork in Namibia where data was collected through interviews and focus groups across six conservancies in the Zambezi Region. Through interviews and focus groups ten wellbeing dimensions emerged. These ten dimensions shed light on two important findings: First, the dimensions are inclusive of many well-explored wellbeing components, which challenges the notion that global indices do not adequately capture the dimensions of wellbeing. However, and secondly, it is how these categories are elaborated that make them useful at the local scale. Therefore, it is not the way wellbeing is categorized that is most important, it is how these dimensions are interpreted and incorporated in the process of conservation planning. These insights are significant because conservation initiatives that are better tailored to local needs can foster more meaningful community involvement, which Namibia’s programme has proved to be integral to conservation success.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".