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Record W1568433324

Constructions of Deficit: Families and Children on the Margins in Costa Rica

2014· article· en· W1568433324 on OpenAlexaff
Victoria Purcell‐Gates

Bibliographic record

VenueGlobal Education Review (Mercy College, New York) · 2014
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLiteracyImmigrationNexus (standard)EthnographySociologyFocus groupGender studiesFamily literacyPedagogyPolitical scienceAnthropology
DOInot available

Abstract

fetched live from OpenAlex

This analysis examines the nexus of marginalization and education, particularly the literacy potential and achievement of young children from socially and politically marginalized communities.Drawing on data from a study of literacy practice among Nicaraguan immigrants in Costa Rica and the schooling of the Nicaraguan children in Costa Rican schools, this analysis reveals the ways that constructs such as difference and deficit are constructed within historical, economic, and cultural contexts, for the most part in the absence of empirical evidence.The data used for this analysis was collected as part of a six-month, ethnographic case study of literacy practice within Costa Rican and the Nicaraguan immigrant communities.Data came from (a) observations in kindergarten, grade 1, and grade 2 classes in a public school near San Jos; (b) interviews with public school administrators and teachers; (c) community observations of literacy practices in Costa Rican contexts and within the precarios where Nicaraguan immigrants live; (d) semi-structured home literacy interviews with Nicaraguan participants from one prominent precario; (e) early literacy assessment results for children in the kindergarten and first grade;(f) expert interviews with administrators of NGOs who focus on the "Nicaraguan problem"; and (g) reading and writing artifacts from the communities and the schools.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.618
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.301
Teacher spread0.285 · 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 designNot applicable
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

Citations0
Published2014
Admission routes1
Has abstractyes

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