¿Qué twiteastes tú? Variation in second person singular preterit –s in Spanish tweets
Bibliographic record
Abstract
Spanish dialects throughout Spain and the Americas have shown variation in the second person singular form of the preterit tense; in certain cases, a non-standard –s is found at the end of the verb conjugation (fuistes, comistes, dijistes, etc). This has been mentioned descriptively by several researchers as a cross-dialectal as well as historical feature of many dialects, yet little empirical data is available on this topic related to what factors constrain the variable. In fact, only one study has looked at this phenomenon from a variationist perspective (see Barnes, 2012). This study borrows certain methodological aspects of Barnes’ (2012) analysis of oral data but applies them to the analysis of the variable as it exists in the written sphere. Data is collected through the social media mogul Twitter and tabulated with the multiple regression logistic software, GoldVarb X (Sankoff, Tagliamonte, & Smith, 2005). The data suggests that verb frequency is the only factor group that significantly conditions one variant over another, with high frequency verbs highly conditioning the standard and low frequency verbs highly conditioning the non-standard. Varios dialectos españoles que se encuentran en España y en la Américas tienen una tendencia de demostrar variación en la forma de segunda persona singular del pretérito; en ciertos casos, se encuentra una –s no-estándar al final de la conjugación del verbo (fuistes, comistes, dijistes, etc). Este fenómeno ha sido mencionado descriptivamente por varios investigadores como un rasgo inter-dialectal y también histórico, sin embargo, no hay muchos datos empíricos disponibles acerca de qué factores lingüísticos restringen la variable. De hecho, solamente un estudio ha explorado el fenómeno desde la perspectiva variacionista (véase Barnes, 2012). Este estudio toma prestado algunos aspectos metodológicos del análisis de data oral de Barnes (2012) pero los aplica al análisis de la variable dentro del ámbito escrito. Los datos se recogen a través la red social enorme Twitter y se tabulan con el programa estadístico GoldVarb X (Sankoff, Tagliamonte, & Smith, 2005). Se encuentra que la frecuencia del verbo es el único factor que significativamente restringe la variable; los verbos de alta frecuencia condicionan el uso de la forma estándar mientras que los de baja frecuencia favorecen la no-estándar.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".