The contribution of a collaborative approach in understanding resistance factors when implementing change
Bibliographic record
Abstract
Students' wellbeing and their educational success are playing an increasingly important role in the policies, decisions, and actions of educational administrators.Therefore, the desire to meet students' needs is the driving force behind changes in the school environment, which can lead to a degree of resistance from educational staff who do not always see the merits of the proposed actions.The aim of this development research is to equip educational administrators with a better understanding of the resistance factors, concerns, reactions, and obstacles encountered when implementing change to promote educational success and well-being in a context of diversity.This development research, carried out with partners representing five francophone schools and community organizations involved with youth in four Canadian provinces, members of the RÉVERBÈRE research network.The research resulted in a preliminary report with the aim of developing a questionnaire to determine the presence of resistance, concerns, and organizational obstacles to change.The questionnaire then developed will be used by educational administrators to demystify and better understand the presence and the types of resistance in their institutions with the objective of establishing an initial portrait of their context reality in order to better support their staff members.This tool will consider current changes with a view to well-being, inclusion, and openness to diversity.In this communication, we most of all highlight the contribution of the development research process to the co-construction of the items in the preliminary report and the accumulation of evidence firstly based on research based knowledge , and then, during activities carried out with our partners.Validating items with partners in the field adds relevance to this report and enriches it with concrete examples from their respective environments where changes are taking place.In a context of major change, we will also highlight the benefits of a partnership approach, i.e. research and development, to encourage collaboration between members of the research and practice communities with a view to better understanding the factors of resistance to change in educational context.
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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 | 0.085 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.028 | 0.019 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".