Hope and Its Distribution in Rural Tanzania
Bibliographic record
Abstract
Recent research at the intersection of psychology and economics sheds light on the influence of hope on economic decisions. A body of that work concentrates on the economics of hope in developing country contexts. We identify two notable gaps: lack of attention to the measurement of hope as a latent psychological construct, and consequently, the lack of description and characterization of hope as a variable that can be measured and targeted. This study addresses these gaps by assessing the effectiveness of a novel hope measurement instrument, utilizing a large primary dataset collected in rural Tanzania. We estimate hope distributions across over 5,000 individuals and conditionally within subgroups defined by gender, region, recent shock, age, food security, income source, and religiosity. A positively-worded question about faith had the greatest information content among all questions, negatively worded questions were more effective in distinguishing people with relatively high hope. Employing generalized structural equation models, we observe significant variations in hope across sub-groups. Correcting for measurement distortions, we find significant heterogeneity in hope distributions across individuals and subgroups. The presence of an income-earning household member and religiosity yield the most pronounced shifts in hope distributions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".