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Record W2324694230 · doi:10.18740/s4831r

Economic Inequality Matters: Reflections on Piketty’s Capital in the 21st Century

2016· article· en· W2324694230 on OpenAlexvenueno aff
Elaine Coburn

Bibliographic record

VenueSocialist studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Economy and Marxism
Canadian institutionsnot available
Fundersnot available
KeywordsCapitalismInequalityPoliticsArgument (complex analysis)Positive economicsLuckNeoclassical economicsEconomicsSocial inequalityEconomic inequalitySociologyCapital (architecture)Social capitalSocial mobilityPolitical economySocial sciencePolitical scienceLawEpistemology

Abstract

fetched live from OpenAlex

In <em>Capital in the 21st Century</em>, Piketty takes a central liberal claim about economic inequality seriously and asks: does capitalism reward merit? If true, we would expect salaries, presumably rooted in the reward of merit in the workplace, to be more important to personal wealth than inherited money and property, which is just luck. He concludes that capitalism does not reward merit more than inherited wealth. Piketty suggests that this is at once a political and moral problem. As such, it cannot be resolved through economics alone, especially in the profession’s current incarnation, characterized by mathematical fetishization. Instead, all of the social sciences and humanities will necessarily be mobilized to develop a full description and analysis of economic inequalities, which must then be made a central question for broad, public debate. This is an important epistemological and political argument, although Capital in the 21st Century has critical weaknesses, including an undertheorized empiricism, a tendency to treat economic inequality as a matter of money and not as a social relationship, and a failure to grasp how class, gender, race and age come together in social relationships of exploitation (and not merely statistical relationship of inequality).

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.786
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.106
GPT teacher head0.410
Teacher spread0.304 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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