Bibliographic record
Abstract
AATB (American Association of Tissue Banks) 35, 223 Aboriginal peoples Canada 132-3 health care 133-5 unemployment rates 134 see also Alaska Natives; First Nations Communities; Native Americans active learning 201-2 college campus intervention 201-5, 214-15 persuasive technique 202-4 self-efficacy 214 acute care hospitals 34 advertising, power of 320 advertising campaigns 28 Advertising Council, Inc. 28 advisory board, research 238-9 African Americans adolescent/mother discussions 277 beliefs 21, 66 donors 36, 37, 53, 64 home-based education strategy 297 kidney disease 20, 64 knowledge of organ donation 74 MOTTEP 49-50 needing transplants 178 organ donation research 10, 48 targeted in St Louis 48 Twin Cities xvi, 64-5, 66 age factors media message placement 46-7 organ donation permission 36, 37 organ donation promotions 258-9 organ donor registration 111-12 organ viability 32, 321 agenda-setting model 45 aided-recall data 54 Alaska Division of Motor Vehicles 86 health care 84-5 oral storytelling 85, 87-8 radio stations 89 regional medical centers 86 rural schools 93-4 teaching the teachers strategy 90-5, 96 tertiary care centers 86 traditional healing practices 85 Alaska Health Fairs, Inc. 86, 89 Alaska Native Medical Center (ANMC) 84, 89, 91 Alaska Native Tribal Health Consortium 87, 133 Alaska Natives xvi, xvii blood groups 84 cultural factors 135 death by injuries 84 disease, chronic 83, 134 handouts/fact sheets 87 health problems 132-3 organ and tissue donation xvi, xvii, 83-8, 132, 135-6 organ donation video 87-9 posters for campaign 87 public health educator 88 resource handbook 89-90
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.806 | 0.741 |
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".