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Record W4391464497 · doi:10.1002/9781394260331.ch9

Class

2018· other· en· W4391464497 on OpenAlexaff
Rob Beamish

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicPolitical Economy and Marxism
Canadian institutionsQueen's University
Fundersnot available
KeywordsClass (philosophy)Computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The concept of class is central to sociology although it is a complex and contested term. The best way to understand class is to focus on four sociologists whose work has most influenced how class is understood. There were sound reasons for early political economists and Karl Marx to identify three classes as the major forces in nineteenth-century Europe. Like Marx, Max Weber studied power and class in capitalist society. He argued that identifiable groups of people exercise power within a community. Drawing upon Marx and Weber, Pierre Bourdieu develops class around three key concepts: field, capital, and habitus. Like Bourdieu, Anthony Giddens' theory of structuration seeks to integrate human agency with elements of social structure. Gender, racialization, (dis)ability, age, and sexual identity, for example, are, along with class, intertwined in the social practices ordered across space and time which constitute social action as a whole.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.887
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1130.030

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 head0.022
GPT teacher head0.315
Teacher spread0.294 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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