A Conceptual Continuous Improvement Framework to Examine the "Problems of Understanding" Applied Research
Bibliographic record
Abstract
Objectives: Improving performance to meet strategic priorities, such as teaching balanced with increased applied research activities, has developed into a central, though contentious, discourse for faculty in Ontario colleges. The aim of this article is to analyze and better understand why faculty are not engaged in applied research practices. Method: This article draws from social cognition theory and a social constructivist perspective. The literature review examines the evolution of colleges in Ontario, including the political factors and symbolic artifacts that shape values and organizational practices. This study sought to explore how a conceptual continuous improvement (CI) framework might advance our understanding of the policy shifts between applied research discourses within Ontario colleges in Canada and barriers that faculty face to enact applied research practices. Results: Underpinned by a set of simple principles, including improving through communication, learning through collaboration, and changing through coordination, the conceptual CI processes and systematic method provide opportunities to bridge the different contexts and unveil the varied on-the-ground realities of faculty teaching and research tasks. Conclusions: The findings reveal developmental needs and adaptive institutional challenges related to applied research practice changes have been influenced by political, cultural, and socio-cognition contexts and tasks. Implication for Practice: The inventive conceptual CI framework provides a viable means to analyze the fragmented state of applied research practices across Ontario colleges, which may ignite conversations and inform decision-making as well as suggest approaches to change at other global postsecondary education institutions. The innovative conceptual CI framework analysis tool will be of interest to faculty, institutional leaders, faculty unions, and policymakers.
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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 | 0.039 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.006 | 0.054 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".