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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".