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Record W3001614959 · doi:10.1002/soej.12420

Big Fish, Small Pond: The Effect of Rank at Entry on Postsecondary Outcomes

2020· article· en· W3001614959 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSouthern Economic Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsWilfrid Laurier University
FundersSocial Sciences and Humanities Research Council of CanadaUniversität Zürich
KeywordsRanking (information retrieval)Rank (graph theory)Fish <Actinopterygii>Educational attainmentPercentage pointHigher educationPoint (geometry)PsychologyStatisticsDemographyDemographic economicsMedical educationMathematics educationEconomicsComputer scienceMathematicsMedicineSociologyFisheryBiologyEconomic growthCombinatorics

Abstract

fetched live from OpenAlex

We study whether a student's rank in her program of study in university affects short‐ and longer‐term educational outcomes. Using student‐level administrative data from four universities across many cohorts, we show that ranking higher compared to students in the same program in the same year has a positive effect on grade point average and lowers the probability of switching programs at the end of the first year, but has small effects on credit completion, departures, and degree attainment. Our results suggest that being the big fish in the small pond produces moderate advantages in higher education.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.314
Teacher spread0.288 · 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