An Eclectic Phraseological Research on the Formation and Degrammaticalization of Phraseological Units
Bibliographic record
Abstract
This paper reveals new features of certain phraseological units (PUs) through bottom-up and top-down examination. Previous phraseological research has tended to focus on the PUs characteristically observed in a genre or context or how PUs semantically and syntactically behave to improve communicative competence. However, previous phraseological research has failed to give systematic explanations as to the formation of PUs and the process and conditions necessary for a word-combination to become a PU. By applying existing word-formation rules to various PUs, this study clarifies the formation and formation process for PUs and the four conditions required for a word combination to become a PU. It was found that although the word-formation rules are used, PUs generally arise in an unformed manner. Further, word-combinations are used as PUs only if they undergo a formation process and abide by the four conditions. PU features were found to be function words from a degrammaticalization standpoint. It has been widely accepted that degrammaticalization occurs in words. However, no research to date has dealt with the application of degrammaticalization to PUs. It was found that semantic degrammaticalization occurs in PUs arising from function words.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.004 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".