Are All Concessive Scalar Particles the Same? Probing into Spanish "Siquiera"
Bibliographic record
Abstract
Concessive scalar particles (CSPs) (Crnic 2011a,b) (Slovenian magari, Greek esto, and Spanish siquiera, among others) are focus sensitive polarity items that get licensed in a variety of non-veridical contexts, where they trigger a characteristic interpretation: CSPs convey a strengthening effect in downward entailing environments, a ‘settle for less’ interpretation in modal contexts, and a negative bias in questions. This paper explores the characterization of this class of items by probing into Spanish siquiera. The paper reveals differences between siquiera and magari that challenge a straightforward extension to siquiera of the analysis of CSPs presented in Crnic 2011a and Crnic 2011b and shows that the analysis of esto in Giannakidou 2007 does not cover siquiera either —partly for reasons already pointed out for magari by Crnic. The central insights of Crnic's and Giannakidou’s work are nevertheless reconciled in an alternative analysis. The picture that emerges is that CSPs might uniformly convey an existential meaning that determines a set of alternatives, but differ with respect to the role that these alternatives play in determining their interpretation and distribution.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".