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Record W2126200597 · doi:10.20360/g2xs3x

'Doing Right' By Melissa: An Inquiry Into School Spaces

2013· article· en· W2126200597 on OpenAlexvenueno aff
Sarah Vander Zanden

Bibliographic record

VenueLanguage and Literacy · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsLiteracyCurriculumSociologyAction (physics)PedagogyCode (set theory)Critical literacyMathematics educationPsychologyComputer scienceSet (abstract data type)

Abstract

fetched live from OpenAlex

This educational ethnographic case study explores Melissa’s literacy experiences in an urban elementary school. Using a modified domain analysis (LeCompte & Schensul, 1999), this paper focuses specifically on what it meant for Melissa to ‘Do the Right Thing’ in various school spaces. Her application of doing the right thing in different spaces complicates what it means to take ‘right action’. Therefore, ‘taking right action’ was divided into three domains: 1) Institutional Domain: Code of Compliance 2) Dominant Literacy Domain: School Code and 3) Personal Domain: Melissa’s Self- Reliance. While Melissa was considered a model fifth grader in her urban elementary school setting by her teachers and peers, her personal code of literacy often subsumed the dominant school discourse of ‘do the right thing’. Analysis generated prospective inroads for understanding how literacy learning is inextricably intertwined with relationships of space and discourse. Insights from this close analysis point to the need for nuanced recognition of students’ intellectual lives and underscore the plague of low expectations that narrowing curriculum imposes upon students.

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 categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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 head0.010
GPT teacher head0.263
Teacher spread0.254 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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