Whose knowledge matters in climate change adaptation? Perceived and measured rainfall trends during the last half century in south‐western Tanzania
Bibliographic record
Abstract
Parts of eastern Africa have experienced substantial climatic variability and extremes during the last few decades. Here we explore the extent to which local place‐based knowledge is used and is relevant to understanding and appropriately responding to place‐based climate variability and change (specifically rainfall) in an area of considerable rainfall variability in south‐western Tanzania. Primary data were collected using focus group discussions and household questionnaire surveys, and secondary data obtained from government institutions. Various changes associated with the frequency, intensity and consistency of rainfall during the period 1960 to 2014 are explored. Findings indicate that knowledge and perceptions associated with climate operate at a local level, and that these are not necessarily applicable to neighbouring regions. Smallholder farmers in the Great Ruaha River Sub‐Basin rely on incremental adaptations of agricultural practices, in response to climatic stresses which have long‐term implications. We argue that incremental adaptations ought to be supplemented by more transformative changes of existing agricultural practices, such as using more climate‐adapted crops and livestock. Moreover, caution is required when examining human perceptions and responses to climate variability and change at the site‐specific scale, as such findings may not necessarily be applicable to broader regions in all cases.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".