LETTERS TO THE EDITOR To the Editor:
Bibliographic record
Abstract
Studies (IJMED, vol. 23, no. 2, pp. 159-161) but I am disappointed at his comment that, in my chapter, I “[go] off at a tangent with [my] discussion of disasters on small islands”. Disasters on small islands are not a tangent to the book’s topics due to the importance to researchers and practitioners of small island disasters, risks, and vulnerabilities. From the vast literature supporting this topic, I give examples of one book (Lewis, 1999) and one paper (Pelling and Uitto, 2001). Also from the research community, Godfrey Baldacchino has illustrated the importance of islands by creating a Canada Research Chair in Island Studies at the University of Prince Edward Island and by founding the Island Studies Journal. Vulnerability and disaster issues will naturally be a component of that work. See also McCall (1994 and 1996) for the justification of nissology, the study of islands on their own terms. If more research evidence would still be of interest regarding the acceptance of small island vulnerability studies as useful and important, see the research outputs from, and sources listed by, the International Small
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.029 | 0.015 |
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".