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Volumes of evidence: examining technical change in the last century through a new lens

2011· article· en· W1559533386 on OpenAlexaffvenue
Michelle Alexopoulos, Jon Cohen

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLens (geology)OptometryHistoryOpticsMedicinePhysics

Abstract

fetched live from OpenAlex

Abstract New indicators of technical change based on titles included in the catalogue of the Library of Congress and on Amazon.com's website are presented along with evidence that they do capture technological innovation. The indicators are used to chart the pattern and nature of technical change over the last century. A strong, causal relationship is found to exist between these indicators and changes in TFP and output per capita. Moreover, innovations in some subgroups have had a greater impact on output and productivity than in others and the key players change over time. Information technologies are currently the dominant subgroup. JEL classificatiion: E32, O3, O4, N1 On présente de nouveaux indicateurs de changement technique basés sur les titres inclus dans le catalogue de la Bibliothèque du Congrès et sur le site de Amazon.com, ainsi que des résultats qui indiquent qu’ils saisissent l’innovation technologique. Les indicateurs sont utilisés pour cartographier le pattern et la nature du changement technique au cours du dernier siècle. On trouve qu’il existe une relation causale forte entre ces indicateurs et les changements dans la productivité totale des facteurs et le produit per capita. De plus, des innovations dans certains sous-groupes ont eu un impact plus grand que d’autres sur la production et la productivité, et les joueurs clés ont changé avec le temps. Les technologies de l’information sont pour le moment le sous-groupe dominant.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.590
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.541
GPT teacher head0.231
Teacher spread0.310 · 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.

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

Citations29
Published2011
Admission routes2
Has abstractyes

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