El Sistema de Clasificación Industrial de América del Norte (SCIAN), ¿un traje hecho a la medida?
Bibliographic record
Abstract
Hace casi 20 anos se iniciaron los trabajos trilaterales con Estados Unidos de America (EE.UU.) y Canada para la construccion del SCIAN y cada dia hay mucho mas que aprender, repasar, precisar, actualizar y dar a conocer sobre este proyecto en constante renovacion. Las actividades economicas que el SCIAN clasifica son dinamicas: los modelos de negocios evolucionan, las empresas diversifican su produccion y buscan innovaciones para ser mas competitivas, las economias se interrelacionan cada vez mas, de manera que se produce un efecto domino en el surgimiento de nuevas actividades economicas; por ello, el clasificador tiene la responsabilidad de revisarse periodicamente y mantenerse a la vanguardia. El articulo aborda aspectos clave del SCIAN, como su ubicacion en el ambito internacional, el origen de su nacimiento y cuatro actualizaciones. Ademas, hace hincapie en lo que clasifica su marco, la normativa y la forma correcta de aplicarlo
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".