Linguistic creativity and discourse profiles of English language children’s novels
Bibliographic record
Abstract
Contemporary discourse studies face the necessity to develop the methods of contrastive sub-discourse analysis which apply numeric and comparable data to diversify and describe sub-discourse types. The aim of the research is to propose a method of discourse profiling serving the purpose, and to further test the method in the contrastive study of linguistic creativity in different types of English language childrens novels. The category of linguistic creativity being the leading form of language poesis receives parametric description on all language representation levels (in written form) and provides the discourse values for contrastive analysis. These values are explored in the fragments of non-autobiographic and autobiographic adventure novel sub-discourse types authored by J.C. George, F. Gibson, J.D. Ullman, and G. Durrell (a total amount of 120.000 signs) annotated manually for 52 linguistic creativity parameters on phonological, morphological, word-formation, lexical, syntactic, and graphic levels. The working hypothesis is that the linguistic creativity parametric activity distributions represent the sub-discourse profiles and may serve to contrast sub-discourse types by means of their vectors contingency values. The analysis in individual parameter activity and in parameter groups activity demonstrated significant variance in sub-discourse construal, with autobiographic sub-discourse of G. Durrell manifesting several higher activity values in word-formation (occasional compounding), lexical use (the use of professional language, lexical tropes, allusive names, higher register style) and syntactic use (the use of parallel structures and syntactic intensifiers). In terms of morphological activity, the parameter values tend to be lower (morphological category shifts), the same stands true of some syntactic (the use of elliptical structures) and lexical parameters (the use of lower register types and proper names). The sub-discourse profiles demonstrate several common features, evidently typical of the discourse type itself, and the features differentiating non-autobiographic and autobiographic discourse subtypes. Vector correlation analysis revealed lower correlation values for autobiographic sub-discourse, which proves its specificity and testifies to the discourse profiling method applicability.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".