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Record W2045378175 · doi:10.3102/0013189x07313156

Pedagogies for the Poor? Realigning Reading Instruction for Low-Income Students With Scientifically Based Reading Research

2007· article· en· W2045378175 on OpenAlexaff
Jim Cummins

Bibliographic record

VenueEducational Researcher · 2007
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhonicsReading (process)Reading comprehensionMathematics educationContext (archaeology)Reading motivationPsychologyLiteracyPedagogyPhonemic awarenessDifferentiated instructionPrimary educationPolitical science

Abstract

fetched live from OpenAlex

In this article, the author argues that there is minimal scientific support for the pedagogical approaches promoted for low-income students in the federal Reading First initiative. In combination with high-stakes testing, the interpretation of the construct systematic phonics instruction in Reading First has resulted in highly teacher-centered and inflexible classroom environments. By privileging these approaches, Reading First ignored the National Reading Panel’s finding that systematic phonics instruction was unrelated to reading comprehension for low-achieving and normally achieving students beyond Grade 1. Also ignored was the significant body of research suggesting that reading engagement is an important predictor of achievement. Alternative evidence-based directions for rebalancing reading instruction for low-income students are suggested in the context of the impending reauthorization of the No Child Left Behind legislation.

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)0.010
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.006
Scholarly communication0.0050.004
Open science0.0010.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.182
GPT teacher head0.530
Teacher spread0.349 · 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 designTheoretical or conceptual
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

Citations135
Published2007
Admission routes1
Has abstractyes

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