Avenues of understanding: mapping the intersecting barriers to adaptation in Namibia
Bibliographic record
Abstract
The existing literature on barriers to adaptation focuses predominantly on the broad, generic factors, such as financial, technological or institutional factors, as examples that might constrain adaptation. Not enough is known, however, about how barriers converge in localities, what drives them and how they interact to affect adaptation processes and outcomes. This paper considers the barriers to adaptation in Namibia through the lens of the ‘adaptation activity space’ – a framework that positions the adapting system in relation to its environment. In doing so, it questions not only what types of barriers are encountered, but what their underlying drivers are and how the relationships among them influence adaptation on the ground. Two intersecting ‘avenues’ within Namibia’s adaptation activity space are explored, namely: (1) the policy-practice partition and (2) the adaptive capacity challenge. Each of these avenues tells a story about the complex nature of barriers and points to the need for greater integration between government spheres, across temporal scales and among actor groups. Such integration is necessary for addressing the barriers to adaptation and for paving the way to a more effective and sustainable adaptation activity space in Namibia.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".