Bibliographic record
Abstract
AB STRACT The article refers to studies indicating that universal old age pension programmes alone or in combination with earnings-related schemes are conducive to poverty alleviation and less income inequality. Universalism matters, but few countries in the world have introduced universal old age pension programmes. The article does not research this apparent paradox, but asks the empirical question of whether poverty was a prime concern and reflected in arguments used in favour of universal old age pension when such programmes were introduced historically. What were the pro-arguments? The article looks at the arguments for establishing universal old age pension in three selected countries, all belonging to the group of pioneer countries in this respect: Canada, Mauritius and Norway, which all introduced universal pensions in the 1950s. Historical arguments for universal pension systems in these countries are presented and compared. The ambition to reduce poverty was an important motivation in two of the countries, but the main consideration cutting across all three countries was the moral aversion to means-testing and the desire to achieve fairness and respect to human dignity. Another argument found in all three countries was the pragmatic one that a universal scheme would lead to a reduction of the administrative cost of old age provision compared with a system based on means testing.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".