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Record W2797217954 · doi:10.1177/016146811812000304

Relationships among Teachers’ Formal and Informal Positions and Their Incoming Student Composition

2018· article· en· W2797217954 on OpenAlexaboutno aff
Chong Min Kim, Kenneth A. Frank, James P. Spillane

Bibliographic record

VenueTeachers College Record The Voice of Scholarship in Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilityContext (archaeology)Academic achievementSocial stratificationMathematics educationPopulationQuarter (Canadian coin)PsychologySociologyPedagogyPolitical scienceSocial scienceGeography

Abstract

fetched live from OpenAlex

Background/Context While some commentators view education as a social mobility mechanism, many scholars argue that schools reproduce rather than challenge social inequality. A vast literature on the role of family background and educational stratification identifies various factors that help account for how schools contribute to reproducing social inequality. Over the past quarter century local, state, and federal policymakers, motivated at least partially by widening race- and class-based achievement gaps, have used standards and high stakes accountability to hold schools accountable for student performance. But the available evidence on the efficacy of these policy instruments in reducing the role of schools in social stratification is mixed. Purpose/Objective This study examines how student assignment to elementary school classrooms is conditioned by teachers’ formal positions and intra-school social networks. We focus on the allocation of teachers to students; teachers are the major resource a school can allocate to influence educational opportunities. Population/Participants Data for this analysis are drawn from a larger study of school leadership and management in one public school district in the southeastern United States. In the 2006–2007 school year, the Cloverville district served 33,156 students, including 16,214 students at its 30 elementary schools. The final sample for our study included 309 teachers with at least 10 students, not including kindergarten and first grade students, in self-contained classrooms across 29 elementary schools in 2007 (one school was removed from the sample due to low response rates). Research Design This study is a longitudinal observational study that includes social network data and multiple regression analysis. We surveyed faculty two times in waves conducted in 2007 and 2008. Data Collection and Analysis The primary source of data was a survey that asked teachers to identify colleagues who provided them with advice and information about reading and mathematics instruction. The dependent variables in our analyses were class average language arts achievement in the spring of 2006, class average mathematics achievement in the spring of 2006, and class average free or reduced price lunch in the spring of 2006. We fit multiple regressions to estimate the extent to which non-random assignment of students to teachers was a function of teachers’ formal leadership positions and their collegial networks. Findings/Results We found teachers who provided more advice and information to their colleagues and who occupied formal leadership positions were assigned higher achieving students. Further, teachers who occupied formal leadership positions were less likely to be assigned students who received free or reduced price lunch. Conclusions/Recommendations Our study findings provide strong evidence that teachers who have more prominent positions in the formal organization of the school and in informal networks are assigned stronger students. Such non-random assignment of students to teachers can contribute to educational stratification.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.325
Teacher spread0.291 · 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 designObservational
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

Citations2
Published2018
Admission routes1
Has abstractyes

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