Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.059 | 0.028 |
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 it