The allocation of taxing rights for highly digitalised business models: in search of a fair and neutral solution
Bibliographic record
Abstract
El objeto de este articulo es analizar el impacto de los modelos de negocio altamente digitalizados en el reparto de los derechos de gravamen. El reto abarca introducir nuevas reglas del nexo y de reasignacion de los beneficios, que requiere de una modificacion para autorizar a los estados de la fuente/mercado y asi cumplir con el mandato post-BEPS de tributar en el territorio donde se ha generado el valor. Los modelos de negocio altamente digitalizados se aprovechan de los datos y contenidos generados por los usuarios para crear valor y obtener beneficios. En opinion del autor, la solucion a este problema debe respetar las condiciones establecidas en el acuerdo marco de Ottawa de la OCDE para la tributacion del comercio electronico y especialmente lo establecido en el mismo respecto a la neutralidad y justicia fiscal. Dada la dificultad que supone implantar un nuevo concepto de establecimiento permanente virtual a corto o medio plazo, el autor propone tres alternativas que respectan la neutralidad y justicia fiscal, atribuyendo a las jurisdicciones de mercado el derecho a gravar: 1) Creando un nuevo tipo de ingreso en base al articulo de los servicios e publicidad de los Convenios. 2) Haciendo una interpretacion expansiva del concepto de canones en el que se incluya como tales los servicios prestados en la nube. 3) Mediante un nuevo metodo de distribucion del resultado residual que tenga en cuenta factores tanto del lado de la oferta como de la demanda.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".