Bibliographic record
Abstract
Este caso ha sido construido con los aportes de Jeniffer Nova Mercado, administradora de empresa. Agradecemos el testimonio y contribución a esta publicaciónde: Juan Diego Mejia Vicepresidente de Operaciones, Fernando Vélez-Gerente de Compras Estratégicas, Juan Carlos Alvarez-Superintendente de Ingeniería y Proyectos, Elizabeth Haggar-Jefe de Gestión Social, Laura Sierra-Coordinadora de gestión de conocimiento y comunicaciones, Jorge Segrera- Superintendente Administrativo, Oscar Vega-Jefe de Seguridad Industrial, Eduardo Peña-Jefe de Contabilidad, Lazaro Navarro-Superintendente técnico. También agradezco la ayuda en la redacción y diagramación de este caso a Rosario Ariza Garcia maestra en Negocios Internacionales e Integración, por sus valiosos aportes en la revisión. Los casos empresariales se desarrollan exclusivamente para servir como base de discusión en el ámbito educativo; en este sentido no ofrecen respaldo a personas ni organizaciones, no pretenden ilustrar el manejo eficiente o ineficiente de las organizaciones públicas o privadas, ni deben considerarse fuente primaria de información.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".