Relationships among Teachers’ Formal and Informal Positions and Their Incoming Student Composition
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".