Bibliographic record
Abstract
I n this issue of the Journal, we acknowledge the contributions of Daphne Lordly, DEd, PDt, FDC, who has authored/co-authored more than 20 papers published in the Canadian Journal of Dietetic Practice and Research in the past 10 years-more than any other author!Daphne's reflection on this accomplishment brings her back to her early days in dietetic practice where she always looked for answers to her practice-based questions.This continued into academia, where she pursued research to answer questions and expand knowledge about her passion, dietetic education.The Journal has provided a unique vehicle for Daphne to communicate practice-based research to members and contribute to the body of professional knowledge.Daphne also feels that the Journal has contributed to her development as a researcher through thoughtful feedback received from reviewers, which has provided opportunities to reshape her thinking and better communicate her research.The national and international interest generated by her publications has been gratifying, and Daphne believes that this mobilization integrates the Journal with the profession, highlighting some of the common interests amongst the healthcare professions.Her enthusiasm for research has helped support students on their journey to conducting research and publishing.In addition to her contributions as a writer, Daphne has been on the Editorial Board of the Journal for the past 16 years.This volunteer work involves providing input and direction involving key Journal decisions that impact how the Journal functions and how research is disseminated.Daphne has also been a longstanding manuscript referee, completing 18 reviews since 2007 alone.Daphne's contributions to our Journal and more broadly to the profession through her scholarship are significant.
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 | 0.022 | 0.081 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.014 | 0.028 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".