Bibliographic record
Abstract
Tita, la leyenda, nace en los fabulosos anos veinte en Mexico Tenochtitlan, a pesar suyo. En su casa del todavia porfiriano Paseo de la Reforma se comia con cubiertos de plata. Tita, la rebelde, unica en su especie, estudia en un secundaria oficial a donde llega con chofer uniformado y a quien despide una calle antes de la escuela. Tita, la que ama la aventura, lider de las guias, la llaman Baguira, la pantera de los ojos azules que vigila en medio de “California”, un gran llano de la colonia Del Vallle. Tita, la ingenua que se asombra con las unas pintadas de su prima. Tita, la siempre nina, tiene la capacidad de gozar con las cosas pequenas como las cajitas, las tarjetas postales o ese maravilloso mundo que guardan las papelerias. Tita, la que se sabe diferente, la que rompe con patrones establecidos, la irreverente, es mandada por su padre a Canada para estudiar administracion y hacerse cargo de las empresas familiares. Alla sabe del frio, del dolor y la soledad. Tita, en contra de la corriente, como siempre ha hecho, regresa a Mexico y se casa con el padre de su primer hijo. Tita, la intrepida, embarazada de “Micharly” conduce autos de carrera, y poco antes de nacer “Mideby” salta obstaculos con su caballo.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".