Relationships between Economic and Environmental Factors, and Labour Migration to Réunion, 1820–1860
Bibliographic record
Abstract
This chapter compliments that of Chap. 4 in examining the applicability of new spatial and temporal paradigms, and the use of Bayesian Network analysis, for interpreting the history of indentured labour migration in the southwest Indian Ocean World (IOW). It does so in the context of human-environment interaction. A core element of the environmental side of this relationship was the monsoon system of winds and currents that was of fundamental importance in shaping the agricultural, migration, and trading regimes of the IOW. Other major environmental factors included El Niño Southern Oscillation (ENSO), cyclonic activity, and volcanism. At the same time, human activity has indelibly impacted the environment. Our findings reveal the significance chiefly of push and pull factors related to the climate and economic incentives to migration in both Madagascar and Mauritius.
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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 | 0.000 | 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 | 0.001 | 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 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".