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Record W115089844

Greater Visibility for R&D. (Corporate Strategy).(research and Development in North American Industry Classification System( (NAICS))(Brief Article)

2002· article· en· W115089844 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueResearch-Technology Management · 2002
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCompetitive and Knowledge Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Coding (social sciences)VisibilityRegional scienceBusinessIndustrial organizationTelecommunicationsComputer scienceGeographySociology
DOInot available

Abstract

fetched live from OpenAlex

Research and development gets a boost in the North American Industry Classification System (NAICS), which has recently replaced the U.S. Standard Industrial Classification (SIC). U.S., Canadian and Mexican government agencies and businesses use (pronounced Nakes) to track economic activity. The six-digit updates the four-digit SIC which was created in the 1930s and last updated in 1987. More than 350 new industries, such as fiber optic cable manufacturing and satellite communications are recognized for the first time in NAICS. NAICS should do a better job of accounting for these emerging high-tech industries that have been ignored under the old SIC coding system, says Lisa Anderson, an economist at Economy.com, West Chester, Pennsylvania. RD 54171, Research and Development in the Physical, Engineering, and Life Sciences; 54172, Research and Development in the Social Sciences and Humanities. …

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.280
GPT teacher head0.361
Teacher spread0.081 · 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