De la narrativa documental al dato estructurado: anotación semántica con Recogito en un corpus de pintores coloniales en México
Bibliographic record
Abstract
Este artículo presenta un ejercicio metodológico para transformar fuentes históricas narrativas en datos estructurados mediante la anotación semántica con Recogito. En el marco de una investigación doctoral sobre pintores en México (1680–1730), se trabajó con un corpus de 23 documentos parroquiales vinculados a la familia Cuentas, una dinastía activa en varias ciudades novohispanas. Se describe el flujo de trabajo desde la transcripción hasta la exportación de datos, incluyendo el uso de vocabularios personalizados, estrategias de desambiguación y jerarquías de etiquetas. El estudio destaca tanto el potencial de Recogito como herramienta adaptable para generar datasets reutilizables, como los límites inherentes a la fuente seleccionada. Se argumenta que la conversión de lo narrativo a lo tabular es una operación interpretativa que requiere un modelo de datos claro, protocolos consistentes y una lectura crítica de las ausencias documentales.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".