Equity and Excellence: Creating a Socially Responsible Shared Vision of Academic Quality
Bibliographic record
Abstract
The concepts of academic quality and social responsibility in post-secondary organizations are spacious yet becoming more intertwined. With stated organizational values grounded in excellence and diversity, this Organizational Improvement Plan (OIP) seeks to intersect a quality management framework with the equity, diversity, and inclusion (EDI) efforts at a multi-campus college in Atlantic Canada. Specifically, the problem of practice I aim to resolve is how a shared vision of academic quality may be created through an added lens of social responsibility among the academic leadership team. In order to achieve a shared vision of academic quality, there are two main goals central to this OIP: examine the newly developed APAE framework piloted through an international partner through a lens of local community and examine our quality standards through an EDI lens. Organizational learning theory provides insights into understanding how knowledge is created and used within the organization to accomplish these goals. Central to this initiative is a trianalogous leadership framework grounded in servant, transformational, and collective leadership ideologies used to guide the leadership team in a series of Academic Quality Leadership Lessons through a community of practice model. This process is supported by an appreciative inquiry modality which draws upon the strengths of current quality management initiatives and aims to bring about positive change by enhancing the leadership team’s knowledge of socially responsible quality management practices.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.018 | 0.050 |
| Scholarly communication | 0.029 | 0.017 |
| Open science | 0.003 | 0.040 |
| Research integrity | 0.004 | 0.010 |
| 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".