Bibliographic record
Abstract
Change is the one constant in a constantly changing world, including the world of dietetic practice. Over a 40-year career, I have witnessed and participated in many such changes. Key lessons from my early career with Manitoba Agriculture and Manitoba Health include an understanding of the power of teamwork, of the importance of communication skills, of the need for shared knowledge and expertise, and of ways to connect nutrition messages with food and eating. Later, my work as director of education in a family medicine residency program taught me the value of building a portfolio of knowledge and skills and of working with families. Similarly, my work with the Organization for Cooperation in Overseas Development led me to appreciate the need for cultural sensitivity in our work. Opportunities with Dietitians of Canada have shown me that future directions must include continued interdisciplinary development of policy and position papers. Other important challenges include determining issues relevant to various areas of dietetic practice, working to achieve Vision 2020 goals, and inspiring and nurturing new leadership among younger Dietitians of Canada members.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.020 |
| Insufficient payload (model declined to judge) | 0.029 | 0.012 |
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".