Bibliographic record
Abstract
Mary Glover was a Head Nurse at St. Paul's Hospital in Vancouver. She was killed in a plane crash more than 25 years ago. Yet, through this neuroscience nurse's passion for her specialty, we share in her legacy through the annual Mary Glover Lecture, which was established by her parents after her death. The first Mary Glover Lecturer was Pamela Mitchell, a well-known neuroscience nurse from the School of Nursing at the University of Washington. She is leaving a multifaceted legacy through her research on intracranial pressure and quality of care as well as her books and her mentorship. Jessie Young has left a legacy as the founder and first president of the Canadian Association of Neuroscience Nurses (CANN). CANN is leaving a legacy with many firsts among Canadian nursing specialty organizations. Leaving a legacy is not just about donating money or writing a famous book. For most of us, our legacy comes in the little everyday things of life. Ask yourself, what is the legacy that you are leaving as a neuroscience nurse and as an individual?
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".