Oppositionally‐intertwined ecologies: A single‐system, multi‐theory mapping of marginalized students’ experiences
Bibliographic record
Abstract
Abstract This chapter offers a “single‐system, multi‐theory” approach to understanding and improving the “oppositionally‐intertwined” ecologies of marginalized students as they navigate to and through higher education. Drawing from research conducted with Indigenous students in Peru, we use Ecological Systems Theory (EST) as a schematic for visualizing students’ experiences and two theoretical perspectives to illuminate the forces they encounter. We demonstrate how this approach can help educators identify leverage points that can result in both immediate and systemic change to improve educational opportunities and outcomes. Practical Takeaways Marginalized students live in a complex system of multiple, often intertwined forces that both oppress and support them. Ecological systems theory provides a schematic for visualizing forces, tensions, and interconnections, within students’ systems. Using multiple theories to analyze students’ experiences can help educators form more comprehensive understandings of students’ ecological systems and how to affect change within it.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".