Developing an Inclusive Perspective for a Diverse College: Inclusion = Diversity + Engagement
Bibliographic record
Abstract
This article describes a project at the NorQuest College Center for Intercultural Education to develop an inclusion model for a post-secondary, two-year college. Inclusion = Diversity + Engagement is a model for action based on the integration of integral theory, particularly the all quadrants component of the AQAL model by Ken Wilber and the Developmental Model of Intercultural Sensitivity by Dr. Milton Bennett. The author views inclusion as a perspectival phenomenon, socially constructed; a culture of inclusion is, in part, founded on perspective seeking behaviors. Within the model, the focus for translative and transformative change is guided by the Intercultural Competence Stretch Goals document, a map created by the author and her project collaborators to identify selected attitudes, knowledge and skills to support more inclusive communication behaviors. The model is informed by concepts arising out of discourse on inclusion and intercultural competence, specifically on a capacity for perspective taking within a Canadian socio-cultural surround. Within the context of a college with identifiable diversity in terms of country of origin, languages spoken, race, ethnocultural origin including First Nations, Metis and Inuit peoples and the level of ability requiring supports (for physical and/or learning challenges), this article describes an organizational change project sparked by an applied research study to create the Inclusion = Diversity + Engagement model and the organizational change initiatives that flowed out of the model. The applied research question asked: “In what ways might Student Services enhance intercultural communication skills during face-to-face interactions with students.” We found a need to focus on the enhancement of intercultural communication skills based on a primarily ethnocentric, minimization worldview for student services staff. Specific skills included developing a deeper understanding of staff’s own worldview with a focus on identifying preferred communication styles and practicing less familiar, less comfortable styles. We also found a need to practice perspective taking to increase staff capability to check for inclusion in service interactions. Results were used to design inclusion training; the project evolved to develop an integrally informed inclusion map. These organizational change initiatives are continuing through an ongoing inclusion focus at NorQuest College in Edmonton, Alberta, Canada. Although written from a single author perspective, I want to acknowledge the project team members and the community of participants that engaged in this project from project proposal to ongoing inclusion initiatives.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.021 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.003 | 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".