Undermining competition, undermining markets? Implications of Big Tech and digital personal data for competition policy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many countries and jurisdictions are reforming their competition policies in response to growing political and public concerns about market concentration, especially when it comes to the market power of Big Tech firms. A particular concern is the new dynamics in market competition resulting from the rise of digital personal data as the key resource or asset underpinning our economies. Our aim in this paper is to examine the competition policy investigations, proposals, and reforms emerging around the world in order to analyse the policy implications of digital personal data to market competition regimes. Our methodological approach entailed an analysis of policy materials (written in English) produced in various jurisdictions or by international institutions dealing with competition policy. We identified the underlying policy themes, concerns, and processes across these different jurisdictions and institutions in order to understand their concerns about how Big Tech and personal data affect competition and competition policy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it