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Record W2741132191

El Sistema de Clasificación Industrial de América del Norte (SCIAN), ¿un traje hecho a la medida?

2013· article· es· W2741132191 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

VenueRealidad, datos y espacio. Revista internacional de estadística y geografía · 2013
Typearticle
Languagees
FieldSocial Sciences
TopicRegional Development and Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy
DOInot available

Abstract

fetched live from OpenAlex

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.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.298
Teacher spread0.276 · 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