Differences over discourse structure differences: a reply to Urquhart and Urquhart
Bibliographic record
Abstract
Purpose – The purpose of this paper is to respond to Urquhart and Urquhart’s critique of the previous work entitled “Discourse structure differences in lay and professional health communication”, published in this journal in 2012 (Vol. 68 No. 6, pp. 826-851, doi: 10.1108/00220411211277064). Design/methodology/approach – The authors examine Urquhart and Urquhart’s critique and provide responses to their concerns and cautionary remarks against cross-disciplinary contributions. The authors reiterate the central claim. Findings – The authors argue that Mann and Thompson’s (1987, 1988) Rhetorical Structure Theory (RST) offers valuable insights into computer-mediated health communication and deserves further discussion of its methodological strength and weaknesses for application in library and information science. Research limitations/implications – While the authors agree that some methodological limitations pointed out by Urquhart and Urquhart are valid, the authors take this opportunity to correct certain misunderstandings and misstatements. Originality/value – The authors argue for continued use of innovative techniques borrowed from neighbouring disciplines, in spite of objections from the researchers accustomed to a familiar strand of literature. The authors encourage researchers to consider RST and other computational linguistics-based discourse analysis annotation frameworks that could provide the basis for integrated research, and eventual applications in information behaviour and information retrieval.
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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 | 0.074 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.011 | 0.051 |
| Scholarly communication | 0.013 | 0.041 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.032 | 0.064 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".