New perspectives on the study of ser and estar
Bibliographic record
Abstract
1. Ser and estar: Outstanding questions (by Leonetti, Manuel) 2. Ser and estar and aspect 3. More than a copula: complex predicates with estar and the clitic se (by Garcia Fernandez, Luis) 4. Ser, estar and two different modifiers (by Romeu, Juan) 5. Sentences as predicates: the Spanish construction ser (by Fernandez Leborans, Maria Jesus) 6. Ser and estar beyond aspect 7. The inference of temporal persistence and the individual/stage level distinction: the case of ser vs. estar in Spanish (by Gumiel-Molina, Silvia) 8. Location and the estar/ser alternation (by Zagona, Karen) 9. What do Spanish copulas have in common with Tibetan evidentials? (by Camacho, Jose) 10. On word order in Spanish copular sentences (by Leonetti, Manuel) 11. The extension and loss of copulas 12. Origins and development of adjectival passives in Spanish: a corpus study (by Marco, Cristina) 13. Eventive and stative passives and copula selection in Canadian and American Heritage Speaker Spanish (by Valenzuela, Elena) 14. The development and use of the Spanish copula with adjectives by Korean-speaking learners (by Geeslin, Kimberly L.) 15. Index
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.011 | 0.025 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".