Bibliographic record
Abstract
Portuguese Abstract: Este trabalho analisa Ovos verdes e presunto, o quarto livro infantil mais vendido de todos os tempos, como um estudo de caso, para ponderar que a literatura infantil nao-didatizante e uma fonte fundamental do direito. O artigo parte da categorizacao da literatura infantil como constitutiva de regras comportamentais internas junto a crianca-leitora, e dessas regras como cruciais para nortear o comportamento humano. O argumento defendido e de que as regras comportamentais que podem ser sintetizadas a partir da literatura nao-didatizante compartilham as principais caracteristicas das leis e sao inclusive mais fundamentais do que as fontes tradicionais do direito. Por fim, apresentam-se dois exemplos de regras comportamentais que podem ser sintetizadas a partir de Ovos verdes e presunto, uma sobre a importância da persistencia e outra sobre a importância de se manter a mente aberta. English Abstract: This paper uses Green Eggs and Ham, the fourth best-selling children’s book of all time, as a case study to argue that non-didactic children’s literature is a fundamental source of law. It frames such literature as constitutive of internal behavioral rules in the child-reader and these rules as central to guiding human behavior. It argues that the rules of behavior which can be synthesized from non-didactic literature meet all of the main characteristics of law and are more fundamental than traditional sources of law. It, finally, offers two examples of behavioral rules which can be synthesized from Green Eggs and Ham, one regarding the importance of persistence and the other regarding the importance of open-mindedness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".