It’s not just about the cash: The impact of conservation-based employment on human well-being
Bibliographic record
Abstract
Employment is a sought-after conservation-based benefit. The national Environmental Monitor (EM) programme was established in 2013 to address challenges of unemployment and biodiversity conservation adjacent to and inside South African protected areas (PAs). We used qualitative and quantitative methods to interview 109 EMs working in the Kruger to Canyons Biosphere Region, an area encompassing 72 PAs including the Kruger National Park, to document the positive and negative, tangible and intangible impacts of their jobs at an individual, family and community level. We recorded an extensive list of material (e.g. monetary income, improved health and shelter) and psychological well-being impacts (improved self-esteem, empowerment and personal image). Our findings highlight the role of learning new things and having positive social connections in the workplace. We suggest that positive workplace well-being is important for organisational sustainability in the conservation sector and has a role to play in reducing wildlife crime. Conservation implications: Understanding workplace well-being in the conservation sector is important not only for ensuring benefit flow by facilitating personal, family and community well-being, but also for enhancing productivity through increased performance and organisational citizenship behaviour. These findings have direct implications for people and wildlife globally in the context of increasing pressure for PAs to demonstrate their societal contributions, while financial resources for PA management decrease and the illegal use of wildlife inside parks is increasingly becoming a threat to both biodiversity and people.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".