Bibliographic record
Abstract
In North America, during the twentieth century, higher education grew dr amatically in both Canada and the United States. Canada, with a population of 36 million people, now has a higher education system of more than 300 institutions. The United States, with a population of 320 million people, has more than 4,000 higher education institutions. Both systems have a good deal of institutional diversity ranging from small colleges to large researchintensive doctoral-granting universities. The growth in these systems in part was driven by the rise of knowledge-based economies in which a college education has come to be seen as a necessity for those who aspire to professional careers (Grubb and Lazerson 2004). Consequently, demand for higher education throughout North America is relatively high. Canada and the United States both enjoy relatively high postsecondary enrolments. Currently, 65 per cent of all US high-school graduates attend college (Bureau of Labor Statistics 2015), with about 7.3 million attending two-year community colleges and an estimated 13.7 million attending four-year institutions (National Center for Educational Statistics 2015). In Canada, the rate of attendance is also above 60 per cent (Davies and Hammack 2005) with approximately two-thirds of young adults holding a higher education qualification (OECD 2009, 2014).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.021 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.076 | 0.014 |
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".