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Record W1924184558 · doi:10.22329/jtl.v10i1.4171

The Resilience of Deficit Thinking

2015· article· en· W1924184558 on OpenAlexvenueaboutno aff
Curt Dudley‐Marling

Bibliographic record

VenueJournal of Teaching and Learning · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Discipline and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsAppealPovertyPerspective (graphical)CurriculumPsychological resilienceSociologyCritical thinkingResistance (ecology)PsychologyPedagogyPolitical scienceSocial psychologyLawArt

Abstract

fetched live from OpenAlex

Deficit thinking, which situates school failure in the minds, bodies, communities and culture of students, dominates schooling practices in the US and Canada. From this perspective, the remedy to school failure is to “fix” students, their families, culture or language. Critics of deficit thinking point to systemic factors, especially diminished opportunities to learn, to explain high levels of school failure among poor students and students of color. Decades of fierce critique have failed to diminish the appeal of deficit thinking. This paper considers the resistance of deficit thinking to critique by examining the appeal of Ruby Payne’s Culture of Poverty, a particularly egregious instantiation of deficit thinking that pathologizes the language and culture of people living in poverty. The paper then turns to more recent work by the author to counter deficit thinking by showing what happens when students in high-poverty schools are challenged by the kind of thoughtful, engaging, high-expectation curricula common in affluent, high-achieving schools and classrooms.

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)0.006
metaresearch head score (Gemma)0.018
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.049
Scholarly communication0.0070.008
Open science0.0010.012
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.054
GPT teacher head0.392
Teacher spread0.337 · 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

Citations43
Published2015
Admission routes2
Has abstractyes

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