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
ABSTRACT We estimate the impact of temperature shocks on the composition of farm labor in rural Nigeria using a nationally representative household panel survey. Leveraging plausibly exogenous year‐to‐year variation in growing season temperatures, we find that warmer temperatures significantly alter farm labor composition, prompting a substantial shift away from hired labor toward family labor. Interestingly, the displaced hired labor is not easily absorbed into non‐farm sectors in the short term; instead, high temperatures also reduce household participation in local non‐farm wage employment. We further provide suggestive evidence that households reallocate labor in response to temperature shocks because extreme heat renders reliance on external labor economically less viable. In particular, heat stress decreases farm productivity, lowering marginal returns to labor and incentivizing farmers to substitute costly hired labor with household labor. These findings underscore the multifaceted threat that climate change poses to rural livelihoods, reducing not only crop yields but also distorting labor allocation in ways that may further constrain farm productivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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