Bibliographic record
Abstract
Abstract Two empires emerged in Madagascar between the 17th and 19th centuries. The Sakalava Empire, comprising a loose coalition of three kingdoms, embraced most of the west of the island. It was based on control of foreign trade achieved through an alliance with the Swahili and Indian middlemen whose long‐standing maritime trading network connected Madagascar to East Africa, the Red Sea, Persian Gulf, and West India. From the 1750s, rising demand for Malagasy slaves, live cattle, and provisions from the French‐held Mascarenes initially augmented Sakalava dominance. However, from the 1790s, the Merina of the central highlands unified under a dynasty that sought to dominate trade with the Mascarenes, and in 1820 signed a treaty with the British who recognized the Merina sovereign as king of Madagascar and supplied him with the arms and military training required to expand. The Merina initiated military conquests of other regions – in the process ending Sakalava imperial pretensions. However, they aroused enmity and counterattacks from non‐Merina groups, notably the Sakalava, while excessive forced labor undermined the domestic economy and loyalty of Merina subjects. These factors ensured the success of the 1895 French colonial takeover and the end of the Merina Empire.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".