Bibliographic record
Abstract
Being different is neither right nor wrong; it is just different. The dietetic profession as part of society holds many differences. These can be divisive, but learning to recognize the strengths that differences generate could lead to a stronger professional future. Three points arose when reflecting on professional experiences of a career of more than 3 decades. Recognizing different ways of creating and gathering knowledge, leading individuals and teams, and valuing the past as well as the future, will provide opportunities to explore our differences as individuals and as a profession. These themes appear at the intersections of values that could initiate inclusion or exclusion. Learnings from these intersections note that growth can occur even in the midst of adversity. Without understanding the junctions in our professional pathways, futures planning may not build upon the foundation of strengths, experiences, and values present within our profession. Learning to be a risk taker, to walk into the fear, has helped Laurie to shape a career that feels satisfying and successful. Suggested techniques to energize individual careers are provided.
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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.089 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".