Greater Visibility for R&D. (Corporate Strategy).(research and Development in North American Industry Classification System( (NAICS))(Brief Article)
Bibliographic record
Abstract
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. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.270 | 0.163 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".