The Relationship between Discipline and Innovation: A Factor in Professorial Involvement in Integrating Pedagogical Innovation
Bibliographic record
Abstract
The existence of disciplinary culture within universities is rooted in academic tradition. The differences between the disciplines as regards the way in which they perceive and apply Scholarship of Teaching and Learning and the fact that the discipline is a conducive factor to pedagogical innovation invite to explore pedagogical innovation from the disciplinary culture perspective and to question the effect of disciplinary culture on the types of pedagogical innovation professors use. The data for this qualitative research was collected from semi-structured interviews with thirty-two professors, recipients of the Universite de Montreal excellence in teaching award. I used the grounded theory analysis method which has allowed me to uncover similarities and differences between the disciplinary cultures and analyse their impact. The Hard-Pure sciences focus on pedagogical innovation related to the tools, the concept of teaching and the support schemes. The Soft-Pure sciences prefer pedagogical innovation related to tools, support schemes and professionalisation. The Hard-Applied sciences use pedagogical innovation related to tools, pedagogical approaches and professionalisation. The Soft-Applied sciences favour pedagogical innovation related to pedagogical approaches, tools, support schemes and professionalisation. Also, the greatest pedagogical innovation diversity occurs within the Soft-Applied sciences. Thus, it is time for kindling reflection on the influence of the pure versus applied science dimension on pedagogical innovation and questioning ourselves whether the discipline’s relationship with innovation could be a decisive factor in professors’ involvement in integrating pedagogical innovation into teaching? This study finds its significance in probing the influence of disciplinary culture on pedagogical innovation and contributing new knowledge in this field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".