La alternancia de las preposiciones ‘por’ y ‘de’ en las construcciones causales
Bibliographic record
Abstract
El presente trabajo se propone describir, a partir de contextos auténticos, las construcciones causales (CC) introducidas por la preposición ‘de’, en comparación con las construcciones causales introducidas por la preposición ‘por’, en los contextos donde alternan. Se tienen en cuenta los siguientes parámetros de análisis: el tipo de esquema verbal, el tipo de actante(s) involucrado(s) y el tipo de causa introducida. Se espera aportar evidencia empírica a la siguiente hipótesis: las CC introducidas por ‘de’ tienden a aparecer en colocaciones (manifestadas en esquemas intransitivos con verbos de cambio de estado), con actante ‘paciente cambio de estado’ e introduciendo causas reales. En contraposición, las CC introducidas por la preposición ‘por’ aparecen en esquemas verbales transitivos, con actante sujeto ‘agente’, sin constituir colocaciones. Para comprobar las hipótesis, se analizará un corpus oral del español de Buenos Aires, siguiendo una metodología cualitativa y cuantitativa.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".