Identifying ripple effects from new market institutions to household rules -Malawi’s Agricultural Commodity Exchange
Bibliographic record
Abstract
The introduction of new rules in an institutional field provides agents with a new set of opportunities and constraints on which they can leverage to change the rules in other institutional fields. Inspired by Elinor Ostrom, we term this causality a ripple effect, born out of the initial institutional changes. In this article we enquired in what ways women farmers could transfer genderblind changes in the market to the household. We developed a diagnostic tool to capture this propagation of effects and tested our framework with a study of the Agricultural Commodity Exchange for Africa (ACE) in Malawi. We found that the introduction of ACE has produced weak but positive effects for women, some of which rippled the changes in the rules to improve their household situation. Some women see in trading with ACE an opportunity to retain freedom and avoid a constraining married position in the household.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".