Information Technology and the Performance of Higher Education and Training Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study shows that the enrolment rate for the Canadian university system, at 56%, is one of the highest among the member states of the Organization for Economic Cooperation and Development (OECD). This good quantitative performance, however, is not accompanied by a similar qualitative performance in science graduation: only 25% of all university graduates are science graduates – a proportion below that observed in traditional areas (the humanities and social sciences). For computer science graduates, the share is still only 4% in all OECD countries – a paradoxically low proportion in these highly computerized countries. For the Canadian continuing training system, the weakness observable in the quantitative performance (participation rate) is accompanied by a qualitative weakness – the annual average training hours per employee is half the OECD average (31 hours against 64). To reduce the performance gaps between the higher education and training systems, measures are presented which would improve the integration of the two systems. These interventions are considered from the perspective of universities, companies and government.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it