FINANCIAMIENTO PRIVADO CON INVERSIÓN DIRECTA EN CIENCIA. DELINEAN EXPERTOS LAS BASES DE UNA NUEVA POLÍTICA
Bibliographic record
Abstract
FINANCIAMIENTO PRIVADO CON INVERSION DIRECTA A LA CIENCIA, PERFILES DE ACADEMICOS INTER Y TRANSDISCIPLINARIOS, CREACION DE ECOSISTEMAS DE INNOVACION Y LA MEJORA EN LOS PROCESOS DE EVALUACION, SON ALGUNAS DE LA PROPUESTAS VERTIDAS EN LOS FOROS UNIVERSITARIOS LA UNAM Y LOS DESAFIOS DE LA NACION CON EL TEMA CIENCIA, TECNOLOGIA E INNOVACION, ORGANIZADOS POR ESTA CASA DE ESTUDIOS. EN EL TEATRO DE UNIVERSUM, MUSEO DE LAS CIENCIAS, EXPERTOS DE DIVERSAS INSTITUCIONES EDUCATIVAS NACIONALES E INTERNACIONALES ENRIQUECIERON Y FORMULARON LINEAS DE ACCION PARA ROBUSTECER LA POLITICA CIENTIFICA NACIONAL. PARTICIPARON: CESAR AUGUSTO DOMINGUEZ PEREZ-TEJADA, DIRECTOR GENERAL DE DIVULGACION DE LA CIENCIA; JOSE FRANCO, COORDINADOR GENERAL DEL FORO CONSULTIVO CIENTIFICO Y TECNOLOGICO; JULIA TAGUENA PARGA, DIRECTORA ADJUNTA DE DESARROLLO CIENTIFICO DEL CONSEJO NACIONAL DE CIENCIA Y TECNOLOGIA (CONACYT), Y RAUL ROJAS, DE LA UNIVERSIDAD LIBRE DE BERLIN (POR MEDIO DE VIDEOCONFERENCIA DESDE ALEMANIA). EN LA MESA I, MODERADA POR WILLIAM LEE, COORDINADOR DE LA INVESTIGACION CIENTIFICA, TAMBIEN PARTICIPO JOAQUIN RUIZ, DE LA UNIVERSIDAD DE ARIZONA, Y MARIA ELENA MEDINA-MORA, DIRECTORA DEL INSTITUTO NACIONAL DE PSIQUIATRIA. EN LA MESA II, DEDICADA A LA CONDUCCION Y LA VINCULACION DE LOS SECTORES CIENCIA, TECNOLOGIA E INNOVACION, INTERVINIERON ENRIQUE CABRERO, DIRECTOR DEL CONACYT; EL MATEMATICO ALEJANDRO ADEM, DE LA BRITISH COLUMBIA UNIVERSITY DE VANCOUVER, CANADA; EL INGENIERO Y DOCTOR EN COMPUTACION LUIS ENRIQUE SUCAR, INVESTIGADOR DEL INSTITUTO NACIONAL DE ASTROFISICA, OPTICA Y ELECTRONICA Y PREMIO NACIONAL DE CIENCIAS 2016, Y JANA NIETO, DIRECTORA DE RELACIONES INTERINSTITUCIONALES DE LA EMPRESA 3M EN MEXICO. JOSE FRANCO FUNGIO COMO MODERADOR DE ESTA MESA.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".