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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".