Bibliographic record
Abstract
As the Dean of the Schools of Nursing and Allied Health Sciences at the University of Nevada Las Vegas (since 2004) and the Editor of Biological Research for Nursing (since 2002), I was both honored and delighted when Dr. Inouye shared with me that the Asian American/Pacific Islander Nurses Association (AAPINA) was interested in developing a journal and asked me to serve as consulting editor. I immediately arranged a phone call with Daniel Ruth, Senior Acquisitions Editor with Sage Publications, a leading international publisher of journals, books, and electronic media for academic, educational, and professional markets since 1965. After many discussions and a few revisions of the journal proposal, Sage agreed to publish the Asian Pacific Island Nursing Journal (APINJ) as an online open-access journal. This is wonderful because it means that the work of AAPINA members and those of others doing work on the Asian American/Pacific Islander population will have a venue for rapid dissemination of their work. The content is available to readers anywhere and anytime at no cost and may easily incorporate multiple media sources such as links, images and sound. This will improve the research in this area and ultimately improve the healthcare of this population. The business advantage of this model is that this can be done cost-effectively without a guarantee of subscriptions. Online open-access journals have been the subject of debate around recent publishing models. Many professionals have resisted this model and categorized all open-access journals as second rate. Some of these have been referred to as “Predatory” journals by Jeffrey Beall, an academic librarian, because of the high author charges and poor or nonexistent review. This has led to confusion for authors and editors in the nursing profession. Therefore, it is appropriate to distinguish between these predatory journals and APINJ, published by a reputable publisher. The table below provides guidelines for evaluating the integrity of a journal. It has been adapted from “Predatory Publishing: What Editors Need to Know” written by the INANE Predatory Publishing Collaborative and published in the September 2014 issue of Nurse Author & Editor. Please keep these issues in mind when submitting manuscripts to any journals, especially those that you have not heard of or seen. Also, you can rest assured that APINJ will develop and evolve into a substantial journal with integrity. Your submissions to APINJ will assure the success and continuance of this journal. Please keep these issues in mind when submitting manuscripts to any journals, especially those that you have not heard of or seen. Also, you can rest assured that APINJ will develop and evolve into a substantial journal with integrity. Your submissions to APINJ will assure the success and continuance of this journal.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.136 | 0.031 |
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".