Actual Behaviors of Newly Observed Phraseological Units Comprising Two Prepositions
Bibliographic record
Abstract
This paper describes, from a phraseological perspective, phrases created by combining two prepositions into a complex preposition (CP), defined as a word group that functions as a single preposition; for example, into, within, and upon. In particular, this work focuses on the newly observed CPs on against and in to and proposes that be on against and be in to are newly observed phraseological units in contemporary English, which have not yet been described in previous research or English dictionaries. A recent trend in English is to combine two prepositions into new CPs such as in at, and in for. In particular, two adverbial particles in and on co-occur with various prepositions and help establish new CPs. For example, MED2 (Macmillan English Dictionary, 2nd edition) lists new CPs such as in at, in for, in on, on about, and on at. Data obtained from corpora of present-day English show that on against and in to mainly co-occur with be verbs, and be on against and be in to are also observed. However, an extensive literature review shows that previous research and English dictionaries do not address this trend. This research describes their polysemy in different contexts, their functions, formations, and stress patterns.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".