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Record W2028089743 · doi:10.1108/03068291111105138

The puzzles and paradoxes of human need: an introduction

2011· article· en· W2028089743 on OpenAlexaff
Leslie Armour

Bibliographic record

VenueInternational Journal of Social Economics · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Institutions
Canadian institutionsDominican University College
Fundersnot available
KeywordsOriginalityPovertyVariety (cybernetics)Relation (database)Value (mathematics)HappinessSimple (philosophy)SociologyMetaphysicsPositive economicsPhilosophy and economicsEpistemologyFundamental human needsSocial scienceEconomic methodologyEconomicsPolitical scienceEconomic growthLawComputer sciencePhilosophySocial psychologyPsychology

Abstract

fetched live from OpenAlex

Purpose It is difficult to get an adequate account of human needs but there are known needs which, for hundreds of millions of people, are not met. Can the present economic system meet them? Can any economic system meet them? Is simple economic growth the answer? The purpose of this paper is to explore some of the questions, emphasizing the problems and paradoxes. Design/methodology/approach The paper looks at India where poverty is rampant despite recent gains, and at Bhutan which ranks low in economic production but quite high on the “happiness scales”. It also looks at questions of the relation of economic inequality to social problems, citing recent studies. Findings The paper focuses on how well the world's economic systems address, or fail to address, human needs. Originality/value This paper is written by a philosopher and writer on social economics (and Editor of International Journal of Social Economics ( IJSE )) who works in a variety of fields: metaphysics and its epistemological relations, the theory of the history of philosophy (focusing on the seventeenth and nineteenth centuries), and moral, social, and economic philosophy and their relations to culture and religion. The paper then introduces the papers in this special issue of the IJSE devoted to human needs.

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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.272

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.041
GPT teacher head0.246
Teacher spread0.205 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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