Beyond Inclusion: Interrogating the Transformational Potential of Informal STEM Learning
Bibliographic record
Abstract
Makerspaces and education networks are part of the same movement to increase cross-sector collaboration, expand informal STEM learning opportunities, provide professional development, and promote hands-on learning practices. Whereas makerspaces aim to shift power away from instructor-driven pedagogy toward user-centered learning, education networks coordinate learning opportunities and resources throughout a geographic region. In this dissertation, I examine how leaders of makerspaces and education networks grapple with the complexity of intersectionality in attempting to create opportunities for empowerment, skill development, and STEM equity with a particular focus on promoting feminism and anti-racism. In doing so, I examine the possibilities and limitations for how these entities may advance equity in education more broadly, which I understand as supporting culturally sustaining practices and redistributing resources in favor of learners of color, learners with disabilities, girls in STEM, rural learners, and learners in poverty. Chapter 1 explains my motivation for writing this dissertation and outlines the relationship between makerspaces and education networks. Chapter 2 provides an overview of literature on education networks and describes five mechanisms that networks use to achieve their goals. Chapter 3 focuses on a case study of one regional education network and discusses how participation has influenced the work of members. Chapter 4 examines how a feminist makerspace might be a model for more equitable STEM learning environments. Chapter 5 explores how an ecosystem approach supports informal STEM learning through interviews with feminist makerspace leaders from the United States and Canada. I conclude with the main takeaways from these studies as well as a reflection on what intersectional feminist networks might look like in practice.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.037 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| 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".