Bibliographic record
Abstract
La aplicación de la estadística es fundamental para el diseño, análisis e interpretación de la información en las diversas formas que tiene la ciencia de realizar sus investigaciones. En esta oportunidad tengo el agrado de presentar esta reseña sobre el libro Estadística para la investigación de mi amigo y colega Federico Nave, el cual es una compilación valiosa de artículos publicados, en su mayoría, en el Boletín de Investigación y Posgrado de la Universidad de San Carlos de Guatemala. La invitación inicial que se hiciera a Federico Nave para que escribiera un artículo sobre estadística en el boletín divulgativo de la Digi obtuvo una respuesta inmediata, entusiasta y constante, que llevó a que en todos, menos en tres de ellos, contáramos con un artículo de diferentes temáticas. El libro tiene además artículos inéditos que complementan una colección original de importante valor académico.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.010 |
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; both teacher heads agree on what is shown here.
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".