Los verbos fraseológicos especializados en el ámbito de los museos
Bibliographic record
Abstract
Este artículo pretende contribuir a describir fenómenos vinculados con la dinamicidad en los discursos especializados. En concreto, analizamos el comportamiento de verbos fraseológicos en el ámbito de la museología y de la museografía en un corpus de lengua española y mediante la ayuda de la herramienta Termostat (Drouin, 2003). Para ello, partimos de la caracterización de los verbos fraseológicos que transmiten conocimiento especializado propuesta inicialmente por Lorente (2007) y ampliada en AUTOR/A (2008, 2014), así como de la metodología de análisis utilizada en AUTOR/A (2008, 2014, 2017, 2018). A parte de la caracterización de algunos sentidos propios al ámbito de los museos (aspecto que permitiría orientar lexicográficamente durante una compilación terminográfica del ámbito museístico), nuestro objetivo principal es confirmar que la propuesta de elementos relevantes en la distribución del valor terminológico en los verbos fraseológicos es pertinente en otro ámbito de especialidad y para otra lengua románica que los trabajados en AUTOR/A (2008, 2014).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".