1 2014 Budget Recommendations to the Ontario Ministry of Finance
Bibliographic record
Abstract
Canadians are living longer, healthier lives than previous generations, but many older Ontarians still face the prospect of retirement and employment insecurity, insufficient caregiver support, a patchwork of homecare services, as well as rising cost of drugs. At the same time, many single seniors continue to struggle making ends meet and saving for retirement. By 2016 Ontarians over 65 will account for a larger share of Ontario’s population than children aged 0–14. One in six Ontario citizens is already over the age of 65. In Ontario there are currently 2 million people over the age of 65 and the number is projected to more than double to 4.2 million by 2036.i The Ontario government has recently targeted the varied needs of older citizens, particularly in legislated work-leave for family caregivers, supporting CPP enhancement, recognizing the health, well-being, and independence of older Ontarians in the action plans, Living Longer, Living Well and Action Plan for Seniors, and by moving toward stronger investor protection by agreeing with the federal and British Columbia governments on a common securities regulator. These positive steps forward and acknowledgements of the needs of older Ontarians require further direct action.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.099 | 0.030 |
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".