Borderland education beyond frontiers: Policy, community, and educational change during times of crisis
Bibliographic record
Abstract
In this paper, we put center stage the story of a community in the borderland of Palomas and Deming, two twin towns located across the border from each other. In regular times, almost a thousand children crossed the checkpoint every day from Palomas in Mexico to Deming in the United States to attend school. During the COVID-19 pandemic, accessibility to education has been almost completely denied for students living in Mexico. This paper unpacks the findings from a critical case study focused on the school leadership of the community and marks the beginning of a larger action-research initiative aimed at forging alliances with and among community stakeholders, researchers, and community leaders to bring transformative change. Findings suggest that these borderland cities do not view themselves as divided by a physical or ideological Frontera or Barrera. Rather, they see themselves as a unified community whose members live on both sides of the border. The Palomas-Deming borderland community shares one mission of creating the necessary conditions to provide educational equity for all students in the region with U.S. passports regardless of a student’s country of residence. Within these contexts, our paper adds to the sparse scholarship on borderland education and highlights community-based needs for and capabilities of transformative educational change that we perceive as the pathway to more equitable opportunities for learning.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.031 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".