The Inter-Jurisdictional Language of Quality Assurance: Comparing Theory and Practice
Bibliographic record
Abstract
This is a comparative study of two reports on the assurance of quality in higher education that appeared contemporaneously in two similar and closely connected jurisdictions. Using NVivo summative content analysis software, documentary analysis, archival records, WTO submissions, and focus groups and interviews the paper identifies and compares several recurring areas in which nomenclature is at least nominally mutual, such as: the boundary line between academic support services and student services, balancing commonality and diversity, the institution versus the basic academic unit as the focus and scope of assurance, self-regulation versus system regulation, the assurance of quality versus the enhancement of quality, the role of league ranking, performance indicators, and benchmarking, aggregation. Seen in terms of theory-driven evaluation, the study suggests that finding a trans-jurisdictional common ground for quality assurance is more advanced in theory than in practice.
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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 | 0.106 | 0.202 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.007 | 0.045 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".