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Record W4382244703 · doi:10.11647/obp.0361.19

Socialism

2023· book-chapter· en· W4382244703 on OpenAlexaff
Martin J. Osborne, Ariel Rubinstein

Bibliographic record

VenueOpen Book Publishers · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicPolitical Economy and Marxism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProductivityAxiomSocialismConsumption (sociology)Ideal (ethics)EconomicsProduction (economics)Function (biology)Mathematical economicsMicroeconomicsEconomic systemMathematicsEconomic growthPolitical scienceSociologySocial scienceLaw

Abstract

fetched live from OpenAlex

Consider a society in which each individual can produce the same consumption good, like food, using a single input, like land. Each individual is characterized by her productivity. The higher an ndividual’s productivity, the more output she produces with any given amount of the input. An economic system can be thought of as a rule that specifies the output produced by the entire society and the allocation of this output among the individuals as a function of the individuals’ productivities. Should individuals with high productivity get more output than ones with low productivity? Should two individuals with the same productivity receive the same amount of output? Should an increase in an individual’s productivity result in her receiving more output? The design of an economic system requires an answer to such questions. The approach in this chapter (like those in Chapters 3 and 20) is axiomatic. The central result specifies conditions capturing efficiency and fairness that are satisfied only by an economic system that resembles the socialist ideal.

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)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.011
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.014
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.002

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 head0.085
GPT teacher head0.336
Teacher spread0.250 · 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
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
Published2023
Admission routes1
Has abstractyes

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