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Record W2199972268 · doi:10.16967/rpe.v2n2a2

Metodología para el seguimiento y evaluación de los Planes estratégicos departamentales de ciencia, tecnología e innovación, PEDCTI: una propuesta desde el ciclo PHVA

2015· article· es· W2199972268 on OpenAlexaff
Jose Montes, Mónica Leticia Acosta Miranda, Cristhian Ruiz Ramos

Bibliographic record

VenueRevista Perspectiva Empresarial · 2015
Typearticle
Languagees
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

El presente artículo desarrolla una propuesta metodológica para el seguimiento y evaluación de los Planes estratégicos departamentales de ciencia, tecnología e innovación, PEDCTI. Para alcanzar dicho objetivo se revisó la literatura relacionada con el seguimiento y la evaluación de políticas, programas y planes, se construyó una metodología y se diseñó una batería de indicadores de seguimiento y evaluación, y finalmente se realizó un proceso de validación de estos. Este artículo aporta a los tomadores de decisiones y encargados de implementar los PEDCTI una herramienta para el monitoreo de los planes y los resultados de su implementación.

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 imitation

Not 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.

metaresearch head score (Codex)0.050
metaresearch head score (Gemma)0.096
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.050
Threshold uncertainty score0.263

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.096
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0100.012
Science and technology studies0.0020.003
Scholarly communication0.0110.006
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.059
GPT teacher head0.351
Teacher spread0.292 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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