Bibliographic record
Abstract
espanolJuanita, nos muestra su vida desde su mas tierna infancia hasta que por fin, se afinca en Estados Unidos y comienza una manera de vivir de las que se puede denominar normal. Se van sucediendo hechos en diferentes paises: Espana, Brasil, Canada y Estados unidos, que van forjando y perfilando la personalidad de nuestro personaje, que hacen de ella una persona entranable. Esa gran mujer que es Juanita, va desgranando vivencias que se van produciendo en tropel, a veces con dificultades de expresion, pero siempre con confianza, con un carino desbordante y una jovialidad que arrolla a pesar de sus 74 anos. Aunque este relato biografico es el de Juanita, no podemos olvidar a Sixto, su marido y companero, que la ha acompanado y sigue acompanandola en su vida. EnglishJuanita shows us her life starting with her early childhood until she finally settles down in the USA and she sets off a way of living which one could call normal. Various facts happen one after another in several countries: Spain, Brazil, Canada and USA; along this time these facts build up and shape our character's personality, and change her into a cherished person. Juanita, a great woman, goes on telling us her personal experience which gushes forth in huddle, sometimes with an inadequate language command, but always with trust, with an overflowing affection and bursting joviality in spite of her 74 years. Although this is Juanita's biographical story, we cannot forget Sixto, her husband and her companion, who accompanied her and continues accompanying her in her life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".