Dual credit: Creating career and work possibilities for Canadian youth
Bibliographic record
Abstract
In this article, I investigated the problems that educators are addressing with dual credit initiatives and consider tensions that have limited the seamless implementation of dual credit programming. Stakeholder representatives clearly identified common resources and support required to sustain their dual credit programs. Study participants noted the need for strong partnerships for dual credit initiatives, for government policies to allow for articulation of credits between secondary and post-secondary and, in particular, for the means to fund these programs. Findings from individual interviews and focus groups conducted in British Columbia and Ontario with individuals who had formal work-related involvement (e.g., industry associations, government, organized labour, high schools, school boards, colleges, and industry) in dual credit initiatives suggest that access to dual credit options provide secondary students valuable opportunities for future career and post- secondary education, giving support for policies to support stakeholder partnerships to advance the effectiveness of dual credit models. However, with partnerships, tensions exist because stakeholders compete to gain and maintain control of their institutional terri-tories and established standards. Questions arise – “who is going to pay?” and “who is going to benefit?” – that suggest concerns about student access and who is allowed to make use of the resources allotted for these initiatives.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.041 | 0.008 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".