MODELOS ESTADÍSTICOS PARA EL ANÁLISIS FUNCIONAL DE LOS PROCESOS DE GRAMATICALIZACIÓN EN LAS CONSTRUCCIONES PROGRESIVAS DEL INGLÉS
Bibliographic record
Abstract
En este artículo se analizan las relaciones cuantitativas existentes entre las construcciones progresivas del inglés y diferentes patrones funcionales, cuantificables a partir del conjunto de corpora York-Toronto-Helsinki-Penn, en aras de ilustrar los procesos de gramaticalización que experimentan estas construcciones. Se elabora un análisis estadístico que prueba la existencia de relaciones significativas entre los aumentos en las frecuencias de aparición normalizadas de las construcciones progresivas y la evolución de cuatro dimensiones funcionales del modelo multifactorial, así como entre los aumentos en el indicador estimado de los procesos de gramaticalización y tres de dichas dimensiones, en ambos casos, en función del género textual. Asimismo, se describen las principales tendencias de las formas progresivas totales y cinco de sus variantes sintácticas en función de las dimensiones relevantes del modelo multifactorial, así como el período de la lengua.ABSTRACTIn this paper we examine the quantitative relations between English progressive constructions and different functional patterns, measurable on the family of corpora York-Toronto-Helsinki-Penn, in order to illustrate the grammaticalization processes undergone by these constructions. We conduct a statistical analysis that demonstrates significant relationships between the increments in the standardized frequencies of progressive constructions and the changing patterns of four functional dimensions from the multidimensional model, and between the increase in the estimated index of grammaticalization and three of these dimensions, in both cases, according to textual genre. We also describe the main tendencies of the total number of progressive forms and five of its syntactic variants related to the relevant dimensions of the multidimensional model and the historical period.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".