Research Summary: Carving paths of desire; and, Student mobility in Ontario A framework and decision making tool for building better pathways
Bibliographic record
Abstract
The Ontario government has prioritized student pathways within education and between education and the labour market. The Ministry of Training, Colleges and Universities 2015–2016 strategic plan states that “the system will blend academic with applied learning and ensure that transitions are seamless whether it is from high school, between postsecondary education institutions, or between school and work” (Ministry of Training, Colleges and Universities, 2015, p. 3). The government has spent years investing in research and strategy to achieve these ends. In 2011, the Ontario government set out three goals for a province wide credit transfer system to: “expand and improve pathways to respond to student demand; improve transparency and access to information about pathways and credit transfer; [and,] support student success” (Ministry of Training, Colleges and Universities, 2011). At the same time, the Government established the Ontario Council for Articulation and Transfer (ONCAT), a government agency designed to support credit transfer and mobility of Ontario students. With a five-‐year mandate to improve student mobility in Ontario’s public institutions (ONCAT, 2013) the agency has supported research, partnerships and projects to further the systematic capacity for student choice and opportunity. This research was undertaken to support the government’s three goals for pathways in Ontario. Contributing to the four years of substantial research, knowledge building and reflection by ONCAT, this study synthesises current theories and research on student mobility, institutional partnerships and pathways, and presents the current patterns of student flows and institutional agreements in Ontario.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".