Bibliographic record
Abstract
The purpose of this research paper is to contribute to knowledge regarding employer pension plan (EPP) inequality in Canada. Information on EPP coverage and value is analyzed using the 1999 and 2005 Surveys of Financial Security. The results indicate that women, persons who may live alone, landed immigrants, and language minorities are at a disadvantage in their EPP coverage and accrued value. In addition, age, educational attainment, occupation, industry of employment, union membership, total personal income, province, and size of urban residence figure importantly in EPP coverage. Furthermore, age, educational attainment, industry of employment, total personal income, province and size of urban residence are all-important determinants of the termination value of EPPs. To identify inequalities in EPP coverage among the sub-populations, the researchers use multivariate analysis. This allows an identification of inequalities that are not a direct result of differences in age, gender, level of education, location, or position in the labour market. Findings indicate that differences in EPP coverage for women, persons who may live alone, landed immigrants and language minorities are primarily due to differences in these other characteristics. However, the lower EPP value witnessed by these subpopulations cannot be explained by individual or labour market characteristics.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".