Constructions of Deficit: Families and Children on the Margins in Costa Rica
Bibliographic record
Abstract
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 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 kinder, grade 1, and grade 2 classes in a public school near San José; (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 kinder 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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".