Investing in a better future: higher education and post-COVID Canada
Bibliographic record
Abstract
Post-secondary education (PSE) is a vital part of civil society and any modern economy. When broadly accessible, it can enable socioeconomic mobility, improve health outcomes, advance social cohesion, and support a highly skilled workforce. It yields public benefits not only in improved well-being and economic prosperity, but also in reduced costs in health care and social services. Canada also relies heavily on the PSE sector for research. During the COVID-19 pandemic, PSE has supported research related to the pandemic response and other critical areas, including providing expert advice to support public health and government decision-making, while maintaining educational programs and continuing to contribute to local and regional economies. But the pandemic effort has stretched already strained PSE resources and people even further: for decades, declining public investment has driven increases in tuition and decreases in faculty complement, undermining Canada’s research capacity and increasing student debt as well as destabilizing the sector through a growing reliance on volatile international education markets. Given the challenges before us, including climate change, reconciliation, and the pandemic, it is imperative that we better draw on the full range of experience, knowledge, and creativity in Canada and beyond through an inclusive, stable, and globally engaged PSE. Supporting PSE’s recovery will be key to Canada’s ongoing pandemic response and recovery. The recommendations in this report are guided by a single goal—to make the post-secondary sector a more effective partner and support in building a more equitable, sustainable, and evidence-driven future for Canada, through and beyond the COVID-19 pandemic.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.035 | 0.007 |
| Scholarly communication | 0.025 | 0.007 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.040 | 0.004 |
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".