How teachers form educational expectations for students: A comparative factorial survey experiment in three institutional contexts
Bibliographic record
Abstract
While schools are thought to use meritocratic criteria when evaluating students, research indicates that teachers hold lower expectations for students from disadvantaged backgrounds. However, it is unclear what the unique impact is of specific student traits on teacher expectations, as different traits are often correlated to one another in real life. Moreover, research has neglected the role of the institutional context, yet tracking procedures, financial barriers to education, and institutionalized cultural beliefs may influence how teachers form expectations. We conducted a factorial survey experiment in three contexts that vary with respect to these institutional characteristics (The United States, New York City; Norway, Oslo; the Netherlands, Amsterdam). We asked elementary school teachers to express expectations for hypothetical students whose characteristics were experimentally manipulated. Teachers in the different contexts used the same student traits when forming expectations, yet varied in the importance they attached to these traits. In Amsterdam - where teachers track students on the basis of their performance and tracking bears significant consequences for educational careers - we found a large impact of student performance. In Oslo - where institutions show an explicit commitment to equality of educational opportunity - teachers based their expectations less on student effort, and seemed to make more inferences about student performance by a student's socio-economic background. New York teachers seemed to make few inferences about student performance based on their socio-economic background.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".