Bibliographic record
Abstract
Demographic change has had major impacts on Canadian society in the postwar period. From maternity wards through all levels of the education system, into housing markets, auto sales and the stock market, the aging of the massive 10 million-strong Boomer generation (born from 1947 to 1966) has left indelible marks. After the Boom came the Bust. Maternity wards and schools emptied, house prices crashed, and auto sales sagged as the Boom generation was replaced by the smaller Bust generation (born from 1967 to 1979) moving through these stages of their lives. Even the return on education is affected by these demographic trends. Today twentysomethings are in short supply and increasingly command higher salaries and signing bonuses in the new economy. Consequently the return on their education will be higher than it has been for the Generation Xers from the later part of the Boom, who were in abundant supply over the 1980s and early 1990s and are now in their mid to late thirties. Over the past twenty years, the cycle reversed as the Echo generation -the children of the Boomers (born from 1980 to 1995) -made their entry through the maternity wards and into elementary and secondary schools. Now tweens and teenagers, their impact can be seen in many sectors, from rising movie attendance to rising transit ridership. This growing teenage market is increasingly capturing the attention of marketing experts throughout North America. By the 1990s the Boomers were becoming too old to start families and, once again, births declined. Not surprisingly, maternity wards emptied and by the late 1990s school closings were commonplace in many districts. Whereas the Boom and Bust profile was widespread over the entire country, the Echo has been more selective in its geographical boundaries, located primarily in Canada’s urban (including suburban) areas and western provinces (plus Ontario). This has resulted in a diversity of educational trends in the provinces over the past two decades, trends that portend new challenges for education in the new millennium. In summary, demographic change encompasses both the movement of generations through their life course and the movement of people across geographic boundaries. Over the postwar period, these demographic movements have presented all governments with major funding and planning challenges, resulting in a myriad of responses. What lies ahead? Are there clues to the future challenges posed by demographic change in our history? What might be some appropriate responses?
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.061 | 0.006 |
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".