A Grounded Theory Analysis of the Academic and Professional Roles in Assessing the University of Calgary’s Application to Offer a Doctor of Veterinary Medicine Program
Bibliographic record
Abstract
Before a postsecondary provider advertises or offers a for-credit credential in Alberta, it must obtain government approval to offer the program. In the professions, programs are also accredited by the regulating body’s educational committee. This dual-assessment phenomenon was investigated by undertaking a thorough examination of a program assessment exercise in which one government regulatory agency (Campus Alberta Quality Council) and one professional accreditation body (American/Canadian Veterinary Medical Association Council on Education) concomitantly reviewed a proposal to offer a Doctor of Veterinary Medicine at the University of Calgary. Based on data obtained from organizational documents and assessment reports, media accounts, informal conversations and nine interviews, the study inductively identified a basic social structural process in accordance with classic grounded theory methodology. This is one process that responds to the main concern of effectively assessing a program proposal. A highly iterative analysis identified seven themes which were most pronounced in the data. These categories: consistency, classification, interdependence and autonomy, new quality dimensions, culture, peers and personalities, and training were consolidated into four conceptual constructs: standardizing, relating, adapting, and socializing. From these, abstraction allowed for a constructed logic in which both replication and contextualization occur, to different degrees across a spectrum. The core variable, ‘blueprinting’, is enacted by the synergies resulting from replicating and contextualizing. Blueprinting allows for a relatively predictable experience while at the same time, accommodating differences as they arise as a result of the involvement of varying institutions and agencies, programs and participants.
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 | 0.029 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".