A Search for a Measure of the Quality of Life on Prince Edward Island: An Inter-Provincial ‘Cost of Living’ Inquiry
Bibliographic record
Abstract
Can one come up with the ‘hard science’ to show that part of the enviable ‘quality of life’ in certain provinces in Canada has to do with the differential ‘purchasing power’ of their residents? Although the mean level of earnings/income per annum is lower/higher in certain provinces than others, (and such mean earnings may be lower in the rural areas than in the urban ones), yet expenditures and overall costs of consumption may be lower/higher. Various persons think they are; and various others think they aren’t, and neither party appears (so far) to have come up with systematic statistics to back their position or refute the alternative: usage of data is sketchy, anecdotal and fragmented, at best. Thus, by way of example, lot or property purchases and rents, gas bills, professional services, and University tuition costs on Prince Edward Island are presumably amongst the lowest, if not the lowest, in the country. But so are average wages. Moreover, the cost of food, white goods, as well as the levels of provincial taxation, is presumably higher. The fuel/gas bill has also been getting increasingly higher these past couple of years. This very focused study will come up with a measure of the cost of living, or ‘household financial health’, and use this to compare the state of affairs in the various provinces of Canada.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".