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Record W3011465268 · doi:10.1080/08865655.2020.1735480

Creating Change in Higher Education Through Transfronterizx Student-led Grassroots Initiatives in the San Diego-Tijuana Border Region

2020· article· en· W3011465268 on OpenAlexvenueno aff
Vannessa Falcón Orta, Gerald Monk

Bibliographic record

VenueJournal of Borderlands Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsGrassrootsPolitical scienceEconomic growthSociologyEconomicsLawPolitics

Abstract

fetched live from OpenAlex

The purpose of this participatory action research study was to ignite change in higher education institutions through grassroots student-led initiatives focused on creating inclusive campus environments for Transfronterizx college students at the San Diego-Tijuana border region. A total of 15 stakeholders participated in this study, 11 Transfronterizx college students, and four faculty and higher education professional allies. The data of this participatory action research study was collected through a cyclical approach in five different phases consisting of one-on-one interviews and focus groups. The process of implementing institutional change in higher education through student-led initiatives is illustrated in the findings of this study that parallel Elliot’s ([1991]. Action Research for Educational Change. McGraw-Hill Education.) five phases of participatory action research: (a) Identifying and clarifying the general idea; (b) Reconnaissance; (c) Constructing the general plan; (d) Developing the next action steps; and (e) Implementing the next action steps. This study led to the inception of the Transfronterizx Alliance Student Organization (TASO) at San Diego State University (SDSU), a grassroots student-led movement dedicated to fostering the success of Transfronterizx college students at the San Diego-Tijuana border region. These findings are further illustrated through the thoughts, feelings and experiences that participants shared about creating institutional change in higher education.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.163
GPT teacher head0.443
Teacher spread0.279 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2020
Admission routes1
Has abstractyes

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