Bibliographic record
Abstract
COMPRAR TECNOLOGIA NO ES UNA BUENA IDEA, LO MEJOR ES DESARROLLARLA Y APRENDER CONTINUAMENTE, ASEVERO JORGE NIOSI, DE LA UNIVERSIDAD DE QUEBEC, CANAD , EN EL AUDITORIO ARTURO ELIZUNDIA CHARLES DE LA DIVISION DE ESTUDIOS DE POSGRADO DE LA FACULTAD DE CONTADURIA Y ADMINISTRACION, AL OFRECER LA CONFERENCIA REDES NACIONALES DE INNOVACION EN CANAD . ANADIO QUE ES UN HECHO QUE LAS EMPRESAS QUE GASTAN EN INVESTIGACION Y DESARROLLO OBTIENEN UN BENEFICIO NETO. LAS ORGANIZACIONES CREADORAS DE NUEVOS PRODUCTOS O PROCESOS EXPORTAN MAS, CRECEN MAS R PIDO, EMPLEAN MAS GENTE Y PAGAN SALARIOS MAS ALTOS, DIJO. SENALO QUE EN CANAD HOY DIA EXISTE UN EFICIENTE SISTEMA DE REDES DE INNOVACION, EL CUAL SURGIO HACE 60 ANOS, APROXIMADAMENTE. CUANDO SE INICIO LA SEGUNDA GUERRA MUNDIAL, EL GOBIERNO DE ESE PAIS TRATO DE SABER QUE EMPRESAS, UNIVERSIDADES Y LABORATORIOS DESTINABAN PARTE DE SU TIEMPO A LA INVESTIGACION. UN ESTUDIO EVIDENCIO QUE LO HACIAN POCOS. LENTAMENTE EMPEZO LA CONSTRUCCION DE LABORATORIOS PUBLICOS, PARA INCITAR A LAS EMPRESAS A CREAR TECNOLOGIA. DESPUES DE SEIS DECADAS, ASEGURO, HOY PUEDE DECIRSE QUE EL SISTEMA NACIONAL DE INNOVACION CANADIENSE FUNCIONA BIEN.
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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.029 | 0.024 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".