Bibliographic record
Abstract
In Canada as in many other developed countries, universities have reconfigured their degree offerings to focus on preparing students for jobs, yet employers are increasingly dissatisfied with the skills of university graduates. Employers deplore shortcomings in fundamental areas such as critical and creative thinking, analysis, and literacy. International studies confirm that competencies in these areas are declining. Neoliberal policies of recent decades instituted a tertiary education funding model under which costs are shared between the public and students, now seen as clients, and a regulatory model that opened the market to private colleges and universities. In parallel, university enrolments more than doubled even as secondary schools abandoned formal teaching of the literacy skills needed to prepare students for university study. This massification of tertiary education led to the dismantling of the academic programs that honed literacy and intellectual skills in favour of applied programs more suited to the lower literacy of the new cohorts. Given that the funding and admissions models of publicly supported universities now preclude the offer genuinely academic degrees, it will fall to the private sector to move beyond the creation of private applied colleges to support the establishment of independent universities dedicated to offering academic programs.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".