Bibliographic record
Abstract
This thesis proposes syntactic and semantic analyses for the two kinds of pseudoclefts, predicational and specificational.I suggest that although the two are syntactically quite different they are similar in their semantics.Predicational pseudoclefts are analyzed as predicational copular clauses with a free relative subject and a predicative counterweight.In contrast, I adopt a deletion-based approach to specificational pseudoclefts, in which the pre-copular constituent is left-dislocated and the counterweight is a fragment of what is underlyingly a full clause.Semantically, I propose that the wh-clause in predicational pseudoclefts denotes an individual, while in specificational pseudoclefts it denotes a question.The analyses of both wh-clauses involve the maximal informativity operator, MAX INF .In the former, MAX INF operates over predicates and in the latter it operates over sets of propositions.The overall aim of this thesis is to account for the differences between predicational and specificational pseudoclefts while also highlighting their similarities in an intuitively satisfying manner.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".