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Record W3204862073 · doi:10.1080/15595692.2021.1974383

Rural indigenous students in Peruvian Urban higher education: interweaving ecological systems of coloniality, community, barriers, and opportunities

2021· article· en· W3204862073 on OpenAlexafffund
Kayla M. Johnson, Joseph Levitan

Bibliographic record

VenueDiaspora Indigenous and Minority Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Policies and Reforms
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousSociologyTraditional knowledgeEcologyEconomic growthHigher educationGeographyEnvironmental planningPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

In Peru, Indigenous students from rural communities must often migrate to urban areas to access higher education. Navigating to and through urban higher education is a complex task where Peru’s oppressive colonial legacies intertwine with students’ community values, resources, and strengths. How can we more deeply understand the interconnecting systems that oppress and support rural Indigenous students, and how can we mobilize these understandings to reimagine higher education? In this paper, we use photo-cued interviewing and ecological systems theory to 1) make sense of rural Indigenous students’ experiences as they navigate to and through higher education in urban areas and 2) uncover levers for systemic change to improve higher education policy and practice. In doing so, we expand beyond a two worlds perspective of Indigenous educational experiences to offer a more holistic view on coloniality and Indigenous resilience 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.006
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.333
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2021
Admission routes2
Has abstractyes

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