Conceptualization and Operationalization of Accountability in Canadian Education: A Systematic Review
Bibliographic record
Abstract
In this systematic review, I explore the conceptualization and operationalization of accountability within Canadian K-12 education systems. I suggest that while the frameworks of local jurisdictions remain in Economic-Bureaucratic Accountability (EBA) model, there is a shift from Economic-Bureaucratic Accountability model to Ethic-Professional Accountability (EPA) model in current conceptualization and operationalization of accountability in Canadian education systems. The mechanism of solely using standardized testing programs for accountability has shifted to a multidimensional accountability framework that includes a wide range of indicators and the use of program evaluations to engage and empower key stakeholders in K-12 system and to ensure student success. However, continuous empirical inquiry is needed on current accountability concepts and frameworks to gain more reflective thoughts on refinements and evolvement of accountability in Canadian education systems.
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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 | 0.054 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.026 | 0.034 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| 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".