Características de las tesis de pregrado de Obstetricia de la Universidad Nacional Mayor de San Marcos 2018-2022
Bibliographic record
Abstract
Determina las características de las tesis de pregrado de Obstetricia \nde la Universidad Nacional Mayor de San Marcos 2018-2022. Es un estudio descriptivo, cuantitativo, observacional, \ntransversal, retrospectivo de fuentes secundarias. La población de 221 tesis de \npregrado de Obstetricia. La técnica fue documental y se utilizó una ficha de \nrecolección de datos. En los resultados, las características generales fueron, el 92.8% de los tesistas eran \nde sexo femenino, el 7.2% eran de sexo masculino, el 100% de las tesis tenían \nun solo autor, el 69.2% publicó su tesis en Cybertesis. Además, un total de 221 \ntesis de pregrado se sustentaron del 2018 al 2022, en el 2018 se sustentaron el \n27.15% tesis del total; el 2019, 31.22%; el 2020, 12.22%; el 2021, 16.29% y el \n2022, 13.12%. Se concluye que las características más frecuentes de las tesis son tesistas \nmujeres, con un solo autor, desarrollando tesis correlacionales, cuantitativas, \nobservacionales, prospectivas y transversales, investigando más sobre salud \nmaterna, siendo el año 2019 con mayor cantidad de tesis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".