MétaCan
Menu
Back to cohort
Record W4410370377 · doi:10.1177/23294965251338469

Cultural Capital in Higher Education: A Case Study of Extension Requests

2025· article· en· W4410370377 on OpenAlex
Jiasheng Liang, Jonathan Horowitz

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSocial Currents · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsCultural capitalExtension (predicate logic)Capital (architecture)SociologySocial capitalDemographic economicsPolitical scienceEconomicsSocial scienceGeographyComputer science

Abstract

fetched live from OpenAlex

Recent literature conceptualizes accommodation-seeking behaviors as a form of cultural capital. However, quantitative research on this type of cultural capital is limited. In this paper, we quantitatively examine a particular form of cultural capital—asking for extensions on assignments or tests. We present empirical findings from a short survey to post-secondary students at a large research university in Canada. We find evidence that higher-socioeconomic status students negotiate institutional rules more often by asking for more extensions, and that cultural capital could be transmitted via social networks. We do not find evidence that cultural capital leads to higher academic achievements. Furthermore, we find evidence of interdisciplinary variations in how social class is associated with cultural capital. We discuss how our findings extend scholars’ theoretical understanding of cultural capital and how cultural capital reproduces social inequality.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.082
GPT teacher head0.433
Teacher spread0.352 · 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