Bibliographic record
Abstract
This study explores the global discourse surrounding ex-ante regulations, particularly their impact on digital markets. While proponents tout their benefits, concerns are raised about potential negative effects on incentives for major internet platforms and innovation. The paper delves into the challenges of striking a balance between fostering a competitive environment and avoiding overly restrictive laws that could stifle emerging companies in India’s digital marketplaces. It highlights changes in various countries, including the European Union, Germany, Australia, South Korea, the United States, the United Kingdom, and Canada. Emphasizing the importance of precise ex-ante laws, the study urges regulators to develop a strategic approach that simultaneously promotes innovation, competition, and consumer welfare, ensuring a robust digital market. The paper recommends that India proactively create a tailored framework that encourages healthy competition while advancing its goal of building a strong digital economy.
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 | 0.035 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.051 |
| Scholarly communication | 0.023 | 0.042 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".