At the intersection of science, technology, engineering, and mathematics and business management in Canadian higher education: An intentional equity, diversity, and inclusion framework
Bibliographic record
Abstract
Abstract In this study, the authors address the persistent discrimination cis women face in the Canadian science, technology, engineering and mathematics (STEM) higher education context. Pulling on the notion of interrelationships that cross educational faculty boundaries and on intersectionality scholarship to unsettle the structural and disciplinary domains of power, the authors ask, “How can business education and STEM education work together with respect to social considerations, such as gender/race/ethnicity/etc., and social equity and inclusivity, within the Canadian higher education system?” This study aims to build on these interrelationships among diverse, complex individuals who participated in a graduate‐level STEM and business management summer institute to provide an evidence‐based and intentional equity, diversity and inclusion (EDI) framework for STEM higher education contexts. Using a mixed‐methods approach, which saw data collection via a survey instrument and semi‐structured interviews, the subsequent quantitative analysis points to expanding interrelationships to broader areas beyond STEM and business management programs. The close reading of the collected qualitative data, via antenarrative spirals, elevates the participants' complexities beyond focusing “just” on their intersecting identities to looking at their perceptions of STEM fields, the order that ensues and the potential for the undoing of that order. The findings, results, and analyses of these collected data led to an intentional EDI framework, the main contribution of this study, constructed into three main pillars represented by the figure of a tree: the foundational elements (roots) built on individuals' complexities and experiences of Othering, the interrelationships (trunk) possible across various educational and professional dimensions, and a call to structural change initiatives (branches) with the possibility for growth in other areas. This work then contributes to not only filling a significant literature gap and building awareness regarding EDI concerns in STEM contexts via active interrelationship‐building activities but also to unsettling the structural and disciplinary domains of power by embracing a holistic strategy to address systemic discriminatory practices in the Canadian STEM higher education 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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.046 | 0.067 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.002 | 0.004 |
| 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".