Corpus Pattern Analysis of of-Construction Phrase Transformations to the Genitive
Bibliographic record
Abstract
While it is well known that phrase transformations take place, there has been very little concrete research on phrase transformations and the associated rules. To go some way to filling this gap, this paper used corpus pattern analysis (CPA) to examine of-construction phrases, as exemplified by on the face of it and on its face, and elucidate the syntactic manipulation in the semantic and functional features of on its face. The CPA revealed that on its face was semantically the same as on the face of it (i.e., seemingly), but that the meaning of face, i.e., appearance, had more stress in the on the face of it phrase than the end-focus. Further, on its face was found to more often co-occur with legal lexical items such as constitutional, invalid, and lawful, and to be used more often in legal contexts. The reason on its face was derived from on the face of it was found to be because of the end-focus and the influence of semantically compatible phrases, such as for the sake of ~ and for ~’s sake, on behalf of ~ and on ~’s behalf. However, it should be noted that not all phrases that have of-constructions can be transformed into the genitive; for example, for the life of me does not transform into *for my life because *for my life is most often literally interpreted. It appears that linguistic economy is the most probable reason for phrase transformations from of-constructions to genitive constructions.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".