Bibliographic record
Abstract
Arias Galicia, Fernando, Introducción a la técnica de investigación en ciencias de la administración y del comportamiento, por Susana Hernández Michel.Brom O. Juan, Para comprender la historia, por Susana Hernández Michel.Cirigliano, Gustavo F. J., Dinámica de grupos y educación en América Latina, por Bertha Heredia.Etzioni, Amitai, Organizaciones modernas, por Yolanda Hernández González.Gubern, Román, El lenguaje de los comics, por Juan Manuel Cañibe.Informe sobre datos históricos de la Universidad Nacional Autónoma de México, ante el Primer Congreso Latinoamericano de Universidades en Guatemala, por Jorge Pinto M.Knight, Douglas E., Curtis, Huntington W. y Fogel, Lawrence J. (Editores) Cybernetics, Simulation and Conflicts Resolution, por Héctor Alpízar Rojas.Kobayashi Shigeru, Administración creativa, por Nora M. Bonilla Paris.Lawrence, Paul R. y Jay, W. Lorsh, Organization and Environment, por Jorge Lamothe Ayala.Marcuse, Herbert, Contrarrevolución y revuelta, por Mario Enrique Figueroa.Perroux, Francois, Aliénation et société industrielle, por José Alberto Ocampo.Perroux, Francois, Denizet, Jean, Bourguinat, Henri, Inflation, dollar, eurodollar, por José Alberto Ocampo.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.056 | 0.086 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.174 | 0.068 |
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".