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Record W4389255040 · doi:10.1017/s0265052523000237

THREE SOURCES OF ECONOMIC INEQUALITY

2022· article· en· W4389255040 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial Philosophy and Policy · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInequalityMisfortunePoliticsWelfarePositive economicsPower (physics)Root (linguistics)EconomicsLaw and economicsNeoclassical economicsSociologyPolitical economyPolitical scienceLawComputer scienceMathematicsPhilosophyMarket economy

Abstract

fetched live from OpenAlex

Abstract There are three distinct forces that conspire to produce a great deal of economic misery. We can refer to them, for convenience, as misfortune, unfairness, and improvidence. Political philosophers have often shown an interest in one or another of these, but seldom all three. Furthermore, those who do acknowledge all three have often felt driven to collapse them into one root cause of inequality. My goal in this essay will be to argue that the three are independent of one another, but more importantly, that they each require distinct remedies. This is important for egalitarians because it defeats any attempt to develop a “one-size-fits-all” policy aimed at creating a more equal society. This analysis helps us to understand several of the tensions that arise in our attempts to combat inequality that are often obscured by an overemphasis on the power of redistributive taxation as well as generalized inattention to the way that successful welfare states achieve meaningful progress in promoting greater equality.

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.

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 categoriesScience and technology studies
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 score0.689
Threshold uncertainty score0.999

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 studies0.0020.001
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 head0.094
GPT teacher head0.372
Teacher spread0.278 · 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