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The neoclassical analysis of unemployment

2001· book-chapter· en· W6464 on OpenAlexaff
John Cornwall, Wendy Cornwall

Bibliographic record

VenueCambridge University Press eBooks · 2001
Typebook-chapter
Languageen
FieldSocial Sciences
TopicPolitical Economy and Marxism
Canadian institutionsMount Saint Vincent UniversityDalhousie University
Fundersnot available
KeywordsUnemploymentEconomicsKeynesian economicsMacroeconomics

Abstract

fetched live from OpenAlex

Introduction Consider the two contrasting perspectives of the normal functioning of an advanced capitalist economy. The mainstream or neoclassical paradigm derives from a belief that the private sector of a capitalist economy is basically self-regulating in some undefined long run; our view is that capitalism is subject to economic and political conflict and to structural change that leads to periodic episodes of poor performance. Throughout the book we shall emphasize two connections: one between the mainstream conception of capitalism as a self-regulating system and its formalization in neoclassical equilibrium analysis and the other between the view of capitalism as a non-self-regulating system and the evolutionary-Keynesian framework developed in these pages. Which of these two analytical frameworks is better suited for modelling historical processes, in particular the unemployment record, can be determined only by a study of the historical record. In neoclassical equilibrium analysis, long-run outcomes are modelled as interactions between endogenous variables constrained by a set of exogenous variables, usually tastes and technologies. The set of exogenous elements is customarily referred to as the ‘supply side’ or the ‘structural framework’ of the model. When neoclassical equilibrium analysis is used as a descriptive device for modelling capitalism's alleged self-regulating properties, it is assumed that there is a unique long-run equilibrium which depends only upon the values of the exogenous supply-side variables. Because of its properties, the equilibrium functions as an ‘attractor’, ensuring that in the absence of shocks a system in equilibrium will remain there.

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: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.037

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 studies0.0020.003
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.032
GPT teacher head0.242
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
Published2001
Admission routes1
Has abstractyes

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