Bibliographic record
Abstract
Abstract This study of a corpus of varieties of Spanish finds that the level of orality of a text is a strong predictor of subject pronoun expression. Following previous studies’ application of orality to interrogative constructions in Brazilian Portuguese and French, an orality measurement was adapted for Spanish and applied to the new corpus Corpus Diacrónico del Español Latinoamericano: Edición de Sujetos (CorDELES). CorDELES was created to investigate the historic development of subject pronoun expression that led to the high rates of overt subject pronouns attested in current varieties of Latin American Spanish, specifically whether overt subject pronoun expression increases following contact with the enslaved Africans brought to the Caribbean during the colonial period. This contact hypothesis was used as a backdrop to investigate the effects of orality on a corpus. Indeed, the inclusion of orality as a predictor in a mixed-effects model found significant effects for a distinction between Spain and the Americas as well as an intriguing interaction between year and orality. These results add to the burgeoning body of work revealing the benefits of accounting for orality in corpus work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".