Introduction to Special Abstract Section on Disseminating New Knowledge and Practices for Asian and Pacific Islanders
Bibliographic record
Abstract
We are very pleased to publish our first special section of student and community abstracts and brief reports. Students of all disciplines and community members interested in disseminating new knowledge and practices submitted their papers. By accepting abstracts as well as short papers, a new opportunity was afforded for students, faculty, and community members to share their scholarship relative to the health of Asian/Pacific Islanders. The Asian/Pacific Island Nursing Journal is pleased to support and promote their scholarly endeavors particularly from those of Asian/Pacific Islander background. Congratulations to S. Robert Spence Jr., a DNP-FNP student at Washington State University, who was awarded the first student scholarship for publication in the APIN student/community papers special issue. He is the first author for the abstract “Gaining Entrée into a Micronesian Islander-Based Community Organization Through Culturally Responsive Team Building and Reflection.”
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.212 | 0.086 |
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".