Bibliographic record
Abstract
Acknowledgements Notes on Contributors Introduction Wendy L. Bowcher and Bradley A. Smith PART A: Intonation: Construing the Textual Metafunction 1. An Investigation of how Intonation Helps Signal Information Structure Gerard O'Grady (Cardiff University) 2. Creating a Parallel Universe: Mode and the Textual Metafunction in the Study of One News Story Annabelle Lukin (Macquarie University) 3. Intonation: Signal of Information Peaks Shan Zhu (Sun Yat-sen University) 4. A Multi-stratal Approach to a Paragraph-like Organisation in Lectures Kazuyoshi Iwamoto (Kyorin University) PART B: The Interface between Written and Spoken Language 5. The Black Hole in Graphology Martin Davies (University of Stirling, retired) 6. The Spoken Interpretation of Written Text Michael Cummings (York University, Canada) 7. Meaningful Reading: Intonation Choices by Native and non-Native English Speakers Reading The Giving Tree Wendy L. Bowcher and Zhu Shan PART C: The Interface between Music and Language 8. A Note for - ed: Comments on the Treatment of - ed in Handel's Messiah David Banks (Universite de Bretagne Occidentale) 9. A Comparative Analysis of the Rap and Sung Voice: Perspectives from Systemic Phonology, Social Semiotics, and Music Studies David Caldwell (National Institute of Education, Singapore) PART D: Modelling Intonation 10. Towards a Systemic Presentation of the Word Phonology of English Paul Tench (Cardiff University) 11. Digital Phonology: Systemic Perspectives Bradley A. Smith, Stefano Fasciani, and Kay O'Halloran (all at The National University of Singapore) 12. The Meanings and Form of Intonation and Punctuation in English: The Concepts Required for an Explicit Model Robin Fawcett (Cardiff University) PART E: Interacting with Systemic Phonology 13. Locating the Limerick Wall Street Irene and the Sonnet On His Blindness in the Semiotic Space between the Body as Signal Generator/Receiver and the Body as Social Interactant William S. Greaves (York University, Canada) Index
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".