PLATFORMS, POWER & ADVERTISING: ANALYSING RELATIONS OF DEPENDENCY IN THE DIGITAL ADVERTISING ECOSYSTEM
Bibliographic record
Abstract
This paper examines how dominant institutional actors exercise power and control over the digital advertising ecosystem. It pursues this inquiry through a case study on the 2021 introduction of Apple’s App Tracking Transparency (ATT) feature—a privacy setting newly integrated in the operating system of iOS mobile devices. Developing this case study, we ask: How do dominant market actors exercise control over the infrastructural layers of the ‘mobile ad stack’ and how do they gain access to end-user data? These questions are addressed through a mix-methods approach that involves (A) analysis of developer documentation provided by Apple, (B) a review of ongoing litigation, and (C) analysis of financial disclosure forms of two ad-driven platforms Meta and Snapchat. This inquiry shows, first, how and why Facebook and Google, each in their own way, have been highly successful in their ability to aggregate both ad inventory and accurate, real-time user data. Second, it demonstrates how ATT blocked the access of advertising platforms to a key part of this real-time user data, while, simultaneously, enabling Apple to gain control over end-users’ mobile data. Thus, the rollout of ATT and its subsequent shifts in revenue and data demonstrate the relational and constantly evolving nature of institutional power in the mobile advertising ecosystem.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.010 | 0.042 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".