Joseph Krumgold’s <i>…And Now Miguel</i> and <i>Onion John</i>: The Temper of the Times and the Encounter with the Other
Bibliographic record
Abstract
Abstract A newcomer to writing for children, Joseph Krumgold revealed an intuitive mastery of what led to success in children’s publishing in the 1950s, winning the American Library Association’s Newbery Medal for distinguished contributions to children’s literature for both of his first two novels: …And Now Miguel (1953) and Onion John (1959). An exploration of the novels reveals what made for distinction at that time, what assumptions about excellence for child readers the novels imply, and in doing so, what ideas they foster about who children are and how they do and should read. This essay reads the novels both in the wider context of bestselling 1950s books that offer theories about changing American values, and in terms of the specific values espoused by children’s writers, publishers and librarians. A consideration of these matters reveals a metafictional relationship between the two novels that enriches the insights they offer into assumptions about children’s reading.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".