Bibliographic record
Abstract
As this chapter was being written, in the spring of 1984, Canada was struggling to emerge from the worst recession since the Great Depression. Clearly, the booming economic times of the 1960s and early 1970s are over. The previously predictable, ever increasing, rate of growth of spending on many social services, particularly education, has stopped. Given the facts that all levels of government are now constrained by large debt loads and that the economic climate has seriously curtailed government revenue expectations, no one expects significant increases in government expenditures for social services in the near future. Indeed, some of yesterday’s ‘sacred cows’, such as education, have begun to lose their untouchable status. In a recent review conducted by Canada’s leading financial newspaper (the Financial Post) it was noted, Education, in particular should receive relatively less, as school enrolment falls. Health care will have to accommodate increasing numbers of elderly people as the population ages, but will have few extra resources to do so. ( Harris, 1983 , p. 32)
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".