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Record W3164444721 · doi:10.1080/21582041.2021.1916575

Social stratification: past, present, and future

2021· article· en· W3164444721 on OpenAlexaff
Jennifer Jarman, Paul Lambert, Roger Penn

Bibliographic record

VenueContemporary Social Science · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsLakehead University
Fundersnot available
KeywordsSocial stratificationSociologyPoliticsInequalityChinaSocial inequalityStratification (seeds)Social scienceHierarchyPolitical scienceLaw

Abstract

fetched live from OpenAlex

‘Social Stratification, Past, Present, and Future’ celebrates the 50th anniversary of the annual Cambridge Social Stratification Seminar. This editorial presents a brief characterisation of the ‘Cambridge school’ approach that has featured prominently through the seminar’s lifetime. Then it discusses the domains and topics explored in this issue – education; intergenerational transmission of inequality; family, work and employment; occupations; migration for work; housing, and political preferences. While most of the papers focus on Great Britain, several papers involve international comparisons, one focuses on stratification in India, and another on China. Collectively, researchers reveal how social hierarchy influences people’s lives, and reproduces fairly stably over time. The papers also contribute to understanding the sometimes counter-intuitive outcomes that challenge those charged with policy development.

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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.022
Scholarly communication0.0080.009
Open science0.0010.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0060.001

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 head0.124
GPT teacher head0.397
Teacher spread0.273 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations1
Published2021
Admission routes1
Has abstractyes

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