Costos pegajosos (sticky costs) en empresas españolas: un estudio empírico
Bibliographic record
Abstract
Entender cómo y por qué cambia el nivel de costos es fundamental para administrar eficientemente una organización. Tradicionalmente, los costos se dividen en fijos y variables, según varíen o no con respecto al nivel de actividad. Sin embargo, los estudios empíricos más recientes muestran evidencia de costos “pegajosos” (sticky costs) porque su respuesta es más fuerte cuando el nivel de actividad incrementa que cuando dicho nivel disminuye. Este artículo comprueba que los costos pegajosos se observan en empresas españolas en el periodo 2005-2007 y que su magnitud es comparable a la documentada en estudios anteriores. Este mismo estudio puede ser replicado en otros contextos internacionales siempre y cuando se disponga de bases de datos completas y públicamente disponibles, lo que representa una interesante línea de investigación.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".