Monitored versus experience-based perceptions of environmental change: evidence from coastal Tanzania
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it