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Record W4413902021 · doi:10.1177/00131245251367421

Urban and Kibbutz Dwellers in Israel: Perceptions Regarding the Importance of Acquiring Higher Education, Viewed Through Bourdieu’s Theory of the Three Capital Types

2025· article· en· W4413902021 on OpenAlexaff
Matan Markovizky, Yoel Shafran, Tagreed Zoabi

Bibliographic record

VenueEducation and Urban Society · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsNorthern College
Fundersnot available
KeywordsPerceptionSocial capitalSociologyHabitusHuman capital theoryCapital (architecture)Human capitalCultural capitalSocial psychologyPsychologyEconomic growthSocial scienceGeographyEconomics

Abstract

fetched live from OpenAlex

Modern society is organized as a social hierarchy, and individuals of higher status enjoy access to various advantages. Pierre Bourdieu, attempting to quantify social hierarchy, argued it can be observed through an individual’s possession of three intertwined, yet distinct, types of capital: economic capital (material resources), cultural capital (level of education) and social capital (social networks). It also known that acquiring higher education (greater cultural capital) is positively correlated with an increase in an individual’s income (economic capital). Our study examined whether population groups of two community types in Israel – Kibbutz (based on socialist principles) and Urban (based on capitalistic principles), held different views regarding encouraging their children to acquire higher education, as viewed through the prism of the three types of capital. We conducted a qualitative study of in-depth interviews with two population groups (15 individuals per group), one residing in Kibbutz, the other in Urban areas. Our study found significant differences in how each group guides their children to acquire higher education, a phenomenon which could increase inequality in Israeli society.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.020
GPT teacher head0.355
Teacher spread0.334 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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