Bibliographic record
Abstract
The present paper is a study of creativity of idiomatic space in the national varieties of English. The defi nition of the term ‘idiomatic space of social interaction’ has been suggested, formulated as “the property of the idiomatic space of social interaction to, either individually or collectively, form new idiomatic units or to adjust them according to the pragmatic and communicative goals of discourse”, and is further investigated in the News on the Web (NOW) corpus evidenced from canonical and non-canonical forms of the idiom have your cake and eat it (too). In total, 1158 instances of the use of the above idiom were investigated in fi ve national varieties of English (British English, American English, Canadian English, Australian English, and New Zealand English). Based on the data obtained, it was discovered that canonical forms of the idiom under analysis prevail over non-canonical forms (79.8% to 20.2%). Further, this was verifi ed with the help of concordance analysis using AntConc freeware corpus analysis toolkit, which showed minor deviations of the idiom from its base form. However, despite being in the minority, the non-canonical forms of have your cake and eat it (too) lend themselves to a rigorous analysis from the standpoint of the conceptual integration theory. One such instance of conceptual blending – to have their cake and eat it and then expect to still have it to eat later on when they’re hungry. And a bag to put it in – was considered in the present paper. The overall analysis has proved the applicability of the conceptual integration theory to idiomatic creativity in terms of idiomatic inputs and blends. Since no direct blends inv olving the above idiom were identifi ed in the NOW corpus, a wider context of the idiom (at least two or three sentences along with the title of the relevant publication) was considered. This provides certain evidence to hypothesize that the conceptual integration theory is not a universal one when it comes to interpreting idiomatic creativity. With this in mind, further quantitative and qualitative analyses are needed to rigorously determine the place of the conceptual integration theory in examining idiomatic creativity. Key words: study of idioms, idiomatic space, corpus analysis, conceptual integration theory, creativity of idiomatic space.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.013 |
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".