The Drivers of Market Integration Among Indigenous Peoples: Evidence From the Ecuadorian Amazon
Why this work is in the frame
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Bibliographic record
Abstract
Knowledge of the driving forces behind indigenous participation in the market is essential for practitioners intending to integrate conservation and development policies in indigenous territories. Nevertheless, empirical research on the determinants of market integration among indigenous peoples is still scarce. This article uses household survey data and multivariate techniques to examine the drivers of market integration among indigenous groups in the Ecuadorian Amazon. We use multiple measures of market integration, including the sale of crops, timber, and wildlife; the use of credit; and participation in wage labor. The results show that the way in which indigenous peoples integrate into the market depends on their endowments of human, financial, and physical capital. More educated households are able to engage in commercial agriculture and nonagricultural wage work, whereas uneducated poor households in communities in conflict with outsiders are pushed to engage in poorly paid agricultural wage work and (often illegal) timber operations.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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