Bibliographic record
Abstract
M E THE R first through a pair ofshoes.Good sturdy Ameri• can-made walking shoesl It was in the summer of 1936 in Mexico; I arrived to attend the Seminar of the Committee on Cultural Relations with Latin America which had assembled in Cuemavaca, and there at the Hotel Marik I delivered to Ema Fergusson a pair of shoes.For some months she had been in Guaterilala gathering material for her new book Guatemala (1936) and had just come to Mexico to leeturefor the Seminar.Having walked out a pair of shoes in Guatemala she requested that someone coming down from Albuquerque bring her a new pair.I did.The transaction was to the point, perhaps somewhat like its originator.This energetic traveler, Ema Fergusson, is a woman of medium height with greying hair always well-groomed and expressive blue-grey eyes which sparkle with an infectious good humo~.Her manner is reserved but charmingly friendly and her graciousness puts at ease whomever she meets, whether it be the Spanish-speaking gardener, an Indian G.I., or a diplomat in formal dress.She is essentially a friendly person, but one whose friendliness carries with it something more than the desire to establish equitable relations with those she' meets.She is much.more than casually interested; her genuine concern is to know the person, his background, manner of living, problems and reactions.She is a student of her fellow ma~, not for reasons of in• quisitiveness, but because she is deeply aware of the conflicts within our society, conflicts of race and religion, conflicts of class and culture, conflicts to which an understanding, friendly approach is sometimes a key.My life, she says, has followed a very logical pattern, one thing leads naturally to another.Indeed, this may be true but the leading is inspired by a liberal outlook, in-75
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.331 | 0.155 |
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".