MétaCan
Menu
Back to cohort
Record W2151732027 · doi:10.1080/1028663032000089507

Singing alone? The contribution of cultural capital to social cohesion and sustainable communities

2003· article· en· W2151732027 on OpenAlexaffabout
M. Sharon Jeannotte

Bibliographic record

VenueInternational Journal of Cultural Policy · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsCanadian Heritage
Fundersnot available
KeywordsSocial capitalCultural capitalCohesion (chemistry)Individual capitalSociologySocial reproductionSingingCapital (architecture)Social mobilityEconomic capitalPublic relationsSocial scienceEconomicsEconomic growthPolitical scienceHuman capitalManagement

Abstract

fetched live from OpenAlex

: Social capital has been defined by Robert Putnam in his book Bowling Alone: The Collapse and Revival of American Community as “features of social organizations, such as networks, norms and trust, that facilitate action and co-operation for mutual benefit.” Cultural capital, as defined by Pierre Bourdieu, has most often been associated with personal interest in and experience with prestigious cultural resources. According to this definition of cultural capital, familiarity with traditional high-culture forms is a defining characteristic of individuals occupying high status positions within a society. In recent years, cultural policy makers have begun to express a stronger interest in the linkages between these forms of capital. This paper focuses on linkages between personal investments in culture and the propensity to volunteer, using data from the Canadian General Social Survey. It concludes that there are collective benefits from investments in cultural capital and that these benefits make a significant contribution to social cohesion.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.006
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.345
Teacher spread0.327 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations164
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Cultural PolicySame topicSocial and Cultural DynamicsFrench-language works237,207