Canadian Oncology Nursing Journal, Vol. 11, no. 2 (spring 2001)
Bibliographic record
Abstract
This column will highlight education and practice issues for research and research application.Our aim is to increase awareness and comfort with research and to demonstrate that research can be user-friendly.Introductory courses in research methods: Are we fostering a positive attitude?CANO is on the web!!!In search of CANO's web pages?Here's how to find us: • go to the Canadian Oncology Society's (COS) website, found at www.cos.ca• when the COS home page appears, click on the number 5 in lower portion of left-hand side of screen; this will take you to the next screen • on the left-hand side of the new screen, click on "Affiliated Societies" • members of COS will appear.Scroll down until you find CANO • click on CANO's name • you will arrive at CANO's home page where the philosophy, mission, and goals and objectives are easily viewed • CANO's home page also allows you to obtain a membership form and/or obtain access to CANO's secure web pages • if you want to obtain a membership form or learn more about membership benefits, click on "Membership Application and Benefits" • to access CANO's secure web pages, click on "CANO members" • follow directions to obtain your password • once you have your password, your journey through CANO's web pages will begin!! Have fun learning more about CANO! Don't forget -your comments are important to us.Please let us know how we are doing by e-mailing the web pages working group at r3kchapman
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.186 | 0.027 |
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".