Learning Across Borders : Nursing Education, Practice, and Transnational Migration in the Long Twentieth Century
Bibliographic record
Abstract
The Consortium for Nursing History Inquiry celebrates the UBC Centennial and explores the history and future of nursing education. Dr. Kathryn McPherson, Associate Professor in Gender, Feminist, and Women’s History at York University and author of the seminal text, Bedside Matters: The Transformation of Canadian Nursing, 1900-1990 presents the keynote lecture. In her lecture, “Learning Across Boarders: Nursing Education, Practice, and Transnational Migration in the Long 20th Century,” McPherson speaks to the way recent international scholarship in nursing history has helped us think more critically about the divisions within nursing education – how questions of nursing education have been caught up in larger political and cultural debates about skill, gender, nationalism, and religion. Following the Dr. McPherson’s lecture, Dr. Veronica Strong-Boag, Dr. Sally Thorne, and Assistant Professor Emerita Ethel Warbinek give a response as a lead-in to discussion with the audience about the future, promise, and persistent challenges of nursing education and academic nursing programs.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.033 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".