¿DÓNDE QUEDÓ EL CURRÍCULUM? PRESENCIA AUSENCIA DE LA CARRERA
Bibliographic record
Abstract
Cuando se recibe la invitación para escribir un artículo, en una primera instancia se tenía todo pensado y se consideró que sería “fácil”, pero los tres nos animamos a escribir, entonces había que combinar experiencias generacionales, experiencias de formas de haber vivido a pesar de haber tenido la misma “caja de mamá” de la cual salieron nuestros conocimientos, además de nuestras pasiones e intereses por lo educativo. El trabajo de pensar en el estilo, pasó en primera instancia por un texto altamente académico, pero al hablar de la caja de mamá, se cambió radicalmente todo, pues optamos por una metodología d la reflexión crítica hermenéutica desde la praxis cotidiana, una variación de la palabra generadora de Freire o la posibilidad de “Un mercado donde quepan muchos mercados” (ASSMANN, 1997), parafraseado por (VILLACORTA, 2008) como “una educación donde quepan muchas educaciones”.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".