Lars Schoultz. In Their Own Best Interest: A History of the U.S. Effort to Improve Latin Americans.
Bibliographic record
Abstract
Lars Schoultz’s In Their Own Best Interest: A History of the U.S. Effort to Improve Latin Americans offers a richly detailed and engaging narrative that refracts a century and a quarter of U.S. involvement in Latin America through the lens of “uplift,” the persistent desire to improve the people of that region. Going beyond institutional history, Schoultz provides an “ethnography” of policymakers to unravel the complex impulses “that underlie the effort to improve other peoples” (6). Based on a bathyspheric dive into U.S. archival materials, memoirs, and correspondence, as evidenced by footnotes that skew toward primary sources, the story is enlivened by many newly unearthed quotations and anecdotes. The result is a history that is fast moving and highly readable even as it addresses changing perspectives on the proper goals of U.S. policy in Latin America, in particular the nearer nations of Central America and the Caribbean. Schoultz cuts a...
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".