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Record W3015022737 · doi:10.5539/ass.v16n4p42

A Study on “Value” Concept of the Austrian School

2020· article· en· W3015022737 on OpenAlexvenueno aff
Yu Feng

Bibliographic record

VenueAsian Social Science · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Institutions
Canadian institutionsnot available
Fundersnot available
KeywordsMarginal utilityValue (mathematics)Value theoryPremiseEconomicsNeed theoryPositive economicsMicroeconomicsMaslow's hierarchy of needsEpistemologyPsychologySocial psychologyMathematicsPhilosophy

Abstract

fetched live from OpenAlex

The marginal utility theory of Austrian School is an important value theory in western economics. Need theory is the basis and premise of value theory. Need, which is equivalent to desire, has two specific connotations: "one of many needs" and "feelings of a certain need". The contribution and innovation of the need theory of Austrian School lies in that it puts forward two classifications of needs through studying the significance of needs, points out that value is only related to the classification of degrees of needs, reveals the general rule that the importance of one need decreases gradually with the increase in satisfaction, and puts forward the core concept of "marginal utility". The Austrian School studies the quality and quantity of value with human needs or desires as the measure of value. In the qualitative aspect of value, whether people's needs can be met is the direct basis for judging the existence of value, and whether it can improve people's life and welfare is the fundamental reason to judge the value of goods. In terms of the quantity of value, economists of the Austrian School propose the principle that marginal utility determines the value of the last unit of goods, completely solve Adam Smith's value paradox and achieve the transcendence of subjective utility theory.

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)0.005
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 score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.010
Scholarly communication0.0050.008
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.058
GPT teacher head0.268
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

Citations0
Published2020
Admission routes1
Has abstractyes

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