Fast driftssted inden for den digitale økonomi
Bibliographic record
Abstract
Abstract:\nTraditionally, multinational companies operating across borders need a physical place of business to\nsell products to foreign customers. The rapid development in the digital economy has changed this\nneed. Digital technologies have made it possible for companies to do business with customers geographically\nlocated far from them without having physical presence in the country. OECD has highlighted\nthis issue in its BEPS project.\nThe analysis states, that a company operating within the digital economy does not qualify for a permanent\nestablishment under current rules except for a very limited number of cases. A company may\nhold a permanent establishment, if the company operates a server in the source country. Nevertheless,\na server often falls within the exemption of preparatory or auxiliary activities. Software does not\nqualify for a permanent establishment according to the current rules. However, case law shows how\nsoftware in certain cases has dictated a permanent establishment in the country where the software\nis being used.\nAlthough a server may be central in the creation of profits, the thesis finds that existing rules for allocation\nof profits to a permanent establishment barely allocate taxable profits to a server because of\nthe lack of physical locations and personal organized in the source country. Consequently, existing\nrules invite companies to minimize overall tax costs by placing a company in a low tax jurisdiction\nusing digital technology to operate in a high tax jurisdiction.\nFinally, the thesis finds that the existing PE definition does not meet the OECD taxation principles of\ne-commerce (Ottawa principles) demanding for an appropriate solution. Analysing six different solutions\nthe thesis finds a final withholding tax on digital transactions most advisable to address the tax\nchallenges of the digital economy. A final withholding tax meets the Ottawa principles, provides a\nKaldor-Hicks efficient solution and seems politically possible to implement.\nBased on the above, the development demands for a revision of the OECD model tax convention in\norder to make it capable of taxing profits where the economic activities are performed and hence\nwhere the value is created.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.582 | 0.435 |
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".