Preliminary Estimates of Good Life Time (GLT) in Canada Using the General Social Survey
Bibliographic record
Abstract
There has been a recent resurgence of interest in measures of social progress and well-being that go beyond the conventional System of National Accounts measures, especially GDP and GDP per capita. In this context, Wolfson and Rowe (2010) have proposed Good Life Time (GLT) as an alternative / complement to traditional economic measures. GLT is based on a generalization of life expectancy and a person is said to be in GLT if they have adequate amounts of health, money, and the time to enjoy them. In this study, we develop a simple approach using public microdata from the 1992, 1998, 2005, and 2010 General Social Surveys. We conclude that issues related to high item non-response rates and lack of time series consistency in many of the key questionnaire items, especially in the money and health domains, likely overwhelm any time series trends obtained in this manner. Microsimulation or synthetic matching are therefore the recommended methods to obtain time series trends of GLT.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".