Bibliographic record
Abstract
Marilyn Phillips, RN, MSN, is presently employed at Community Medical Center in Toms River, New Jersey. She is a Case Manager for a Post Coronary Care Unit, where she has worked for the past 8 years. Marilyn received her BSN from Kean University in Union, New Jersey, and recently graduated with her MSN in Clinical Management from Kean University. Virginia Fitzsimons, RNC, EdD, FAAN, is the PhD Program Coordinator at Kean University in Toms River, New Jersey. She is a graduate of Teachers' College, Columbia University and Hunter College of the City University of New York. In 1981, she was a founding member of the Kean University School of Nursing BSN program, established the MSN program in 1996, and the PhD program in 2014. She is a member of the American Academy of Nursing and a Fulbright Scholar. Address correspondence to Lynn S. Muller, Esq., Muller & Muller, 15 West Main Street, Suite C, PO Box 164, Bergenfield, NJ 07621. If you have an idea you would like to discuss, send your contact information by e-mail and you will contacted by your preferred method. Disclaimer: The information contained in this department is for educational purposes only. It is not legal advice, which can only be given by an attorney admitted to practice in the jurisdiction/state(s) in which you practice. Do you have a question or issue you would like addressed here? Questions are always welcome. We encourage ALL readers to submit questions and/or manuscripts, as well as topics you would like to see addressed in this department. Questions and other inquiries are accepted by e-mail at: [email protected] The authors report no conflicts of interest.
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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.018 | 0.012 |
| Insufficient payload (model declined to judge) | 0.070 | 0.049 |
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".