Bibliographic record
Abstract
Katy: If a guy does that then they're considered a pimp cause they get all these girls. A girl does that and they're considered a slut. (http://www.pbs.org/wgbh/pages/frontline/shows/georgia/video/tpimps.html) And -conjuncts as predictive conditionals The previous chapters have laid out an analysis of the traditionally central class of conditional constructions in English, namely If P, Q conditionals. How much does this help us with the broader range of conditional constructions? Since each construction is motivated by a variety of factors, and in some cases also by other non-conditional constructions, no treatment of If P, Q conditionals is likely to describe enough facts to explain exhaustively how other constructions serve conditional functions in English. However, much of the analysis presented so far does extend readily to the larger family of conditional and non-conditional constructions which can be used to convey conditional meaning. The analysis further provides regular motivation both for the licensing of non-conditional constructions in conditional uses, and for observed constraints on such licensing. To begin with, a mental-spaces approach gives a relatively straightforward explanation for some of the common conditional uses of non-conditional forms. Our analysis would predict that conditional constructions' functions should overlap with the contextually conveyed meanings of other space-building constructions, when alternative-based prediction or other aspects of conditional meanings are provided by the lexical semantics or the context. Conditional uses of and and or conjunction such as those in (1)–(3) (italics ours) support this hypothesis: (1) “We get rid of Coyne and we're clear,” said Hayden eagerly. (WGT. VH.171)[…]
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".