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Record W4220774368 · doi:10.1177/14782103221076642

Borderland education beyond frontiers: Policy, community, and educational change during times of crisis

2022· article· en· W4220774368 on OpenAlexaff
Kristin Kew, Olga Fellus

Bibliographic record

VenuePolicy Futures in Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTransformative learningSociologyScholarshipIdeologyPolitical scienceEconomic growthEquity (law)Public relationsPedagogyPoliticsLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.389
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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