Bibliographic record
Abstract
Affective job insecurity, 101 Affectual solidarity, 308 Aforementioned demographic change, 2 Aforementioned hypothesis, 5 Aforementioned policies, 119 Age, 79, 102 on elders' statesmanship, 423À424 of immigrants, 78 and psychological contract, 478À482 Ryff Scales of psychological well-being by, 68À74 Age-based employment security, 120 Age-based retirement, 126 Age-biased behavioral tendencies, 164 Age discrimination, 166À167, 225, 226 ageism, 537 within healthcare workforce, 541À546 direct, 538 within healthcare setting ageism and, 539 disease types, 540 morbidity and mortality, 540 obstacles, 540 indirect, 538 in workplace, 164À166 See also Ageism Age Discrimination in Employment Act (ADEA), 4, 412, 504 Age discriminatory behavior, 233 Age diversity, 251, 312À313 climate, 172, 173 Ageism, 537 and age discrimination, 537 within healthcare workforce, 541À546 disease types, 541 employment experience, 545 health and social care employers, 543 healthcare discrimination, 541 nurses challenges, 542À543
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.751 | 0.764 |
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".