A WickEd Leadership Approach to Democratizing STEM Education
Bibliographic record
Abstract
STEM Company Inc. (SC; a pseudonym) is an educational services company located in Canada. Over the past 4 years, the organization has worked on an international project portfolio that has reflected a niche area of education that is currently underserviced in the traditional and private educational landscape. SC’s clients have engaged in procuring curriculum, resources, and programming at the intersection of science, technology, engineering, and mathematics (STEM) and equity, diversity, and inclusion (EDI). This focus has caused an organizational challenge as SC’s initial operational framework and business practices were broadly based on human capital development. My intersectional identity as the author of this Dissertation-in-Practice (DiP), the president of SC, a Black woman, and a STEM education expert speaks truth to power through lived experience. It serves as a driving force to address the wicked problem of inequity in the global education landscape by reframing the organization’s mission, vision, and business practices to democratize access to STEM education for underrepresented groups. To do this, SC is embarking on an organizational change initiative to transform itself into a social enterprise by utilizing a WickEd leadership framework to elevate equity and provide high-quality, culturally responsive and representative STEM education opportunities for underrepresented groups. The ethics of liberation and Africentric values, specifically ubuntu (interconnectedness) and umoja (unity) guide the centering of diverse perspectives and the elevation of community-centric values throughout the organizational change implementation. This work also uses the awareness, desire, knowledge, ability, and reinforcement (ADKAR) and appreciative inquiry (AI) frameworks to delineate a structured change process.\nKeywords: WickEd leadership, STEM education, EDI, Africentric values, wicked problem, liberation
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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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.055 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 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".