Bibliographic record
Abstract
Narratives I chose to tell the stories in the first chapter – about Marcel in John Berger's novel, Eloi and Eulalia at Alto, the James Bay Cree and Inuit, the Yavapai Indians and the Bureau of Reclamation, and the Sioux – because they bring out, in various ways and more forcefully than would a general theoretical analysis, some important truths about how humans value and choose. In some, but not all of these stories, the protagonists are unusual; in their choices they were in a minority. But in the form of their valuing and choosing I believe they are not atypical. It might be thought that the attitude that informed the choices of Marcel and Eloi and Eulalia was a remnant or holdover of an attitude to money and commerce that was once common among the European peasantry. John Berger himself mentions the French peasant's “in-built resistance to consumerism.” Juliet Du Boulay talks of the Greek villager's “basic reluctance to buy and sell at all.” Two different studies of rural Spain describe “a deeply rooted feeling against commercial trading” (Susan Tax Freeman) and “a kind of shame in the pure market transaction” (William Christian). All these studies were done in the 1960s and 1970s. Ruth Behar writes that she too found that “something of [this] old European peasant ethic has remained intact” into the 1980s in the village she studied in Spain, at least among the older people.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".