Bibliographic record
Abstract
Index 50 plus initiative, Germany 188 absenteeism in older workers, Netherlands 148 activation rhetoric 94 active ageing 1, 7-16, 62, 127-30, 204 Canada 68-71 in employment 174-200 France 165-72 Netherlands 154, 155 Adult Learning Grant, UK 105 advertisements ban on upper age limits, UK 101 age and the labour market, UK 94 awareness campaigns, Germany 190-91 barriers, removal of 55 legislation 97 limits for pensions, Germany 180 lobby, powerful, UK 96 neutrality policies 199 Age Advisory Group, UK 101 age-based stereotypes, France 171 Age Can Work report, Australia 36 Age Concern, UK 96 age-conscious personnel policy 149, 150, 151 age discrimination 9, 13, 15, 22, 36, 185 France 166 law 47, 190-91 US 114-15 mandatory retirement 73 removal 14 UK labour market 90 workplace 123-4 Age Discrimination in Employment Act, (ADEA) US 123, 127, 133-4 'age free' employment 132, 214 Age Positive website 101 Age Task Force, UK 101 ageing positive 34 societies 7 ageism 5 in Japan 57 in management thinking 211 in workplace 77 age-specific privileges 192 agriculture 77 Amendment of the Older Workers Law, Japan 48-52, 56-8 asbestos, exposure to 158, 160 attitude changing of employers 36 Australian Council on the Ageing 15 Australia, older workers 12, 22-38 awareness raising 91, 99, 100 baby boomers Canada 64 Netherlands 142 retirement 69 in US 111, 158, 164 back-to-work help 103 Barcelona European Council target 9, 10, 112 'benefit partnership' in work 196 benefits eligibility for 69 for older workers, 205 bias against older workers 208, 209 birth rates, decreasing 175 bridge employment, US 128 'bridging allowance' 186 British Telecom (BT) 91 business creating 50 Canada/Quebec Pension Plan 74-5 Canada, work and retirement 62-79 Canadian Job Strategy (CJS) 76
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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.761 | 0.542 |
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".