Bibliographic record
Abstract
Abstract General extenders ( ge s) are elements such as and so forth occurring at the right periphery. On the referential level, they implicate a set, but they also serve a range of discourse-pragmatic functions, such as hedging and interpersonal relations. Some sociolinguistic studies have seen the development of ge s as synchronic grammaticalization involving phonetic reduction, decategorialization, semantic bleaching and pragmatic enrichment, but other studies have found no evidence of ongoing grammaticalization. Historical studies of ge s are few. This paper sets out to fill this gap by studying the rise of disjunctive, adjunctive and bare ge s formed with what – (or/and) what you will, or what, or/and what else, (or) whatever, (or/and) what not and (or/and) what have you . Despite their apparent similarity, these are shown to have quite different sources and histories. Their development conforms to some of the recognized parameters of grammaticalization but is more fruitfully understood from a constructionist approach.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".