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Record W2111769854

From growth to cycles through beliefs

2011· preprint· en· W2111769854 on OpenAlexaff
Christopher M. Gunn

Bibliographic record

VenueRePEc: Research Papers in Economics · 2011
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEconomicsFrontierProductivityBoomValue (mathematics)Function (biology)SunspotAggregate (composite)Scope (computer science)MicroeconomicsMacroeconomicsMathematicsPhysicsEngineeringPolitical scienceComputer science
DOInot available

Abstract

fetched live from OpenAlex

I present a theoretical model where the economy endogenously adopts the technological ideas of a slowly evolving technological frontier, and show that the presence of a "technological gap" between unadopted ideas and current productivity can lead to multiple equilibria and therefore the possibility that changes in beliefs can be self-fulfilling, often referred to as sunspots. In the model these sunspots take the form of beliefs about the value of adopting the new technological ideas, and unleash both a boom in aggregate quantities as well as eventual productivity growth, increasing the value of adoption and self-confirming the beliefs. Moreover, I demonstrate that the scope for these indeterminacies is a function of the steady-state growth rate of the underlying technological frontier of ideas, and that during times of low growth in ideas, the potential for indeterminacies disappears. Under this view, technology becomes important for cycles not necessarily because of sudden shifts in the technological frontier, but rather, because it defines a technological regime for the economy such that expectations about its value can produce aggregate fluctuations where in a different regime they could not.

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)0.008
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 score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.006
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.078
GPT teacher head0.294
Teacher spread0.217 · 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
Published2011
Admission routes1
Has abstractyes

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