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Record W2085913793 · doi:10.17722/ijme.v2i3.109

Getting More Bang for Our R&D Bucks

2014· article· en· W2085913793 on OpenAlexvenueno aff
James E. Smith

Bibliographic record

VenueInternational Journal of Management Excellence · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicResearch, Science, and Academia
Canadian institutionsnot available
Fundersnot available
KeywordsOrder (exchange)Value (mathematics)Rest (music)Emerging technologiesBusinessPolitical scienceLaw and economicsPublic relationsEconomicsComputer scienceMedicine

Abstract

fetched live from OpenAlex

While some may want to argue the point, many believe it is innovation and the resulting technology that provides society with the impetus to advance and to provide the greatest value to the members of that social order.  Some would also argue that advanced or advancing technologies provide the fastest way to improve the health, wealth, and well being of the individual. Independent of these arguments, it is clear that innovation, particularly the game-changers, has had an accelerating impact on the development of almost every social order on this planet. It is through the creativeness of the individual, plus the organized efforts of research and development programs that have allowed the fostering of ever-growing numbers of new innovations in every aspect of society: agriculture, medicine, transportation, communication, etc. What may be of particular note is that some of the earlier and most contributive to the innovation race are currently less than effective than they once were, or possibly others are simply out-running them.  The United States, plus several others, was one of the earlier contributors to the technology revolution. By most of the standard global measures it is clear the US has not maintained the edge in technology and innovation that was, for many decades, the beacon to a large portion of the rest of the world. While the US is not the only country that has allowed the innovation gap to slip and in some cases to reverse, it may be very representative of the reason the rest have also slowed their progress.  More importantly, the reasons may have very little to do with capabilities, resources, education, manpower, etc.  It may simply be managed expectations, thus the purpose of this paper.

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)0.008
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.761
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.000
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 head0.122
GPT teacher head0.464
Teacher spread0.342 · 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
Published2014
Admission routes1
Has abstractyes

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