A View from the Frontlines: Understanding College to University Transfer in Ontario from a Street-Level Perspective
Bibliographic record
Abstract
In Canada’s most populated province, Ontario, Colleges of Applied Arts and Technology (CAATs) and universities were established as two separate sectors (Skolnik, 1995, 2010). While universities have historically had a broad liberal arts and research-focused mandate, colleges in Ontario were conceived in the 1960s to provide skills-based applied education to facilitate job training and support the labour market (Skolnik et al., 2018). Student transfer between the two sectors was not part of the original plan (Jones, 2007). As a result, college to university transfer within Ontario has been complicated for both students and those who advise them (Arnold, 2011). Encouraged by the Ontario provincial government in recent years, attempts to facilitate student transfer resulted in the development of hundreds of individual college to university program-specific transfer agreements (Boggs & Trick, 2009). Due to this complicated patchwork of transfer agreements and individual transfer credit assessments, front-line university admissions advisors have a unique street-level view of CAAT transfer student challenges when attempting to gain access to university programs. Through the interviews of 27 front-line admissions staff from 16 universities, this research identifies hitherto undisclosed factors that might affect the Ontario college transfer process. This study uses two theories to examine the transfer process from the perspective of front-line university advisors. First, aspects of institutional theory, including isomorphism (van Vught, 2008; DiMaggio & Powell, 1983, 2015) and gradual institutional change (Mahoney & Thelen, 2009), are used to understand how front-line advisors navigate institutional policies in Ontario higher education institutions. Second, the theory of street-level bureaucracy (Lipsky, 2010) examines the transfer process from a front-line advising perspective through the implementation of admissions policies and the opportunities for front-line advisor input into new admissions policies. Also discussed are street-level policy implementation, discretion, and decision-making processes used by front-line admissions advisors. Understanding college to university transfer student challenges from the front-line admissions perspective provides valuable insights into creating and implementing university transfer admission processes and policies. It thus provides another layer of understanding of the challenges CAAT students face when transferring between the two tertiary education sectors where a systemwide transfer mechanism does not exist.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.036 | 0.029 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".