Attack of the snowclones: A corpus-based analysis of extravagant formulaic patterns
Bibliographic record
Abstract
The concept of ‘snowclones’ has gained interest in recent research on linguistic creativity and in studies of extravagance and expressiveness in language. However, no clear criteria for identifying snowclones have yet been established, and detailed corpus-based investigations of the phenomenon are still lacking. This paper addresses this research gap in a twofold way. On the one hand, we develop an operational definition of snowclones, arguing that three criteria are decisive: (i) the existence of a lexically fixed source construction; (ii) partial productivity; (iii) ‘extravagant’ formal and/or functional characteristics. On the other hand, we offer an empirical investigation of two patterns that have often been mentioned as examples of snowclones in the previous literature, namely [ the mother of all X] and [X BE the new Y]. We use collostructional analysis and distributional semantics to explore the partial productivity of both patterns’ slot fillers. In sum, we argue that the concept of snowclones, if properly defined, can contribute substantially to our understanding of creative language use, especially regarding the question of how social, cultural, and interpersonal factors influence the choice of more or less salient linguistic 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".