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Record W2791039767

Small Causes, Large Structures. A New Look at the Roots of Social Inequality in Human Societies

2018· article· en· W2791039767 on OpenAlexvenueno aff
Bernd Baldus

Bibliographic record

VenueAlternate routes · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Institutions
Canadian institutionsnot available
Fundersnot available
KeywordsInequalityDistributive propertyContingencyDistributive justicePositive economicsSociologySocial inequalitySocial structureEpistemologyEconomic JusticeNeoclassical economicsEconomicsSocial psychologyPolitical sciencePsychologyLawPhilosophyMathematics
DOInot available

Abstract

fetched live from OpenAlex

Theoretical discussions of the causes of social inequality still rely largely on frameworks first formulated in the 19th century by Marx, Spencer and Durkheim. For very different reasons these authors arrived at similar conclusions which influenced much subsequent theorizing: inequality structures in human societies were believed to arise from single causes, to impose themselves inevitably on human affairs, and to follow predictable developmental pathways. This paper proposes a new theory which explores elements missing from conventional theories: the role of contingency and chance in the growth of inequality structures, self-reinforcing dynamics and processes of intentional social control which consolidate them, and the indeterminate historical pathways of the evolution of distributive structures. These characteristics also suggest the need to explore alternatives for social change and distributive justice.

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)0.003
metaresearch head score (Gemma)0.007
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: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.035
Scholarly communication0.0060.015
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.056
GPT teacher head0.266
Teacher spread0.210 · 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
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

Citations1
Published2018
Admission routes1
Has abstractyes

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