Monitored versus experience-based perceptions of environmental change: evidence from coastal Tanzania
Bibliographic record
Abstract
The impacts of climate change are likely to exacerbate many problems that coastal areas already face. In this study, we used multinomial logistic regression to examine human perception of climate change based on a cross-sectional survey of 1253 individuals in coastal regions of Tanzania. This was complemented with time series analysis of 50-year meteorological data. The results indicate that self-rated ability to handle work pressure, self-rated ability to handle personal pressure and unexpected difficulties, age, region and educational status were significant predictors of perceived temperature change unlike ethnicity and gender. A disproportionately large percentage of respondents of all ages indicated that temperature was getting hotter between the past 10 and 30 years. This observation was supported by the time series analysis. Although respondents also alluded to changes in rainfall patterns in the past 10–30 years, time series analysis of rainfall revealed a different scenario except for Mtwara region of Tanzania. Because there is agreement between respondents' perceptions of temperature and available scientific climatic evidence over the 50-year period, this study argues that when meteorological records are incomplete or unavailable, local perceptions of climatic changes can be used to complement scientific climatic evidence. Based on the spatial differentials in climate change perception observed in this study, there is opportunity for a more locally oriented adaptation dimension to climate policy integration, which has hitherto been underserved by both academics and policymakers.
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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 | 0.008 |
| 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 | 0.000 |
| 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".