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Record W2480060627

New and Promising Ideas in the Productivity Field: Review Article on Think Like an Enterprise: Why Nations Need Comprehensive Productivity Strategies by Robert Atkinson

2016· article· en· W2480060627 on OpenAlexvenueno aff
Don Drummond

Bibliographic record

VenueInternational productivity monitor · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityManifestoEconomicsPublic economicsGovernment (linguistics)Order (exchange)Industrial organizationEconomic growthMarket economyFinance
DOInot available

Abstract

fetched live from OpenAlex

The most interesting part of Robert Atkinson’s (President of the Information Technology and Innovation Foundation (ITIF)) manifesto on productivity is his recommended productivity strategy for strengthening productivity growth. Atkinson concludes that socalled framework policies are necessary but far from sufficient. The more active public policy role he calls for would include measures to lower the price of capital, moral suasion of firms to think more strategically, more direct government involvement in developing and disseminating new technologies and facilitating interactions across firms and sectors. Many will share Atkinson’s view that different approaches to enhancing productivity growth are in order given the modest productivity records of many countries of late. But it is troubling that Atkinson does not suggest more stringent tests for new policy interventions. They must be subject to benefit-cost analysis to determine whether there is a net social benefit. The experiences countries have had with elements of what Atkinson recommends must be studied to see if they have yielded productivity gains.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.026
GPT teacher head0.339
Teacher spread0.312 · 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 designNot applicable
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
Published2016
Admission routes1
Has abstractyes

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