Please indicate which program you are applying for: n Full-time n Part-time
Bibliographic record
Abstract
a. Why is this program a fit for you at this point in your career? b. What qualities will you bring to ensure success in this program? c. How do you see this degree furthering your professional goals? Note that the quality of your writing and how you express your ideas is an important consideration in the evaluation of your application. 2. A portfolio must be submitted to highlight significant career accomplishments and indicate potential for success in the graduate program. Examples include: Evidence of leadership positions (e.g. peer teaching, management); chairmanship of committees (e.g. Quality Assurance, Best Practice, RNAO sub-committees, CNO or ONA committees); current CNA certification; evidence of the completion of a writing course; post-diploma certificates; or samples of scholarly writing (e.g. a professional paper, recent publication or academic work). Other information reflecting evidence of life long learning and reflective practice may be forwarded. Please indicate the specific documents submitted on the following page. 3. Please attach a photocopy of current Certificate of Competence from the College of Nurses of Ontario and a membership certificate from the Registered Nurses Association of Ontario including liability protection (or equivalent, if you are from another province). 4. Letters of reference (3) are required. One reference must reflect significant contribution to leadership and clinical practice; one reference may be from the individual to whom you have directly reported to at work in the past five years. There also must be one academic reference from a nurse with a master’s or doctoral degree indicating your potential for excellence in academic study.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.532 | 0.453 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".