Analyzing institutional platform power: Evolving relations of dependence in the mobile digital advertising ecosystem
Bibliographic record
Abstract
This article calls for systematic analysis of the accumulation and exercise of institutional platform power in the digital economy. We examine how the relatively open mobile advertising ecosystem is nevertheless dominated by a handful of platform conglomerates, most prominently Google, Facebook, and Apple. Although extant scholarship acknowledges the concentration of corporate power in digital advertising, as well as its cultural, societal, and environmental harms, a comprehensive approach to platform power is missing. Providing a framework to develop such insights, we analyze how shifts in the advertising ecosystem are driven by four interrelated institutional platform strategies: infrastructuralization, platformization, conglomeration, and financialization. The 2021 introduction and subsequent rollout of Apple’s App Tracking Transparency framework serves as an example to demonstrate that even though institutional relationships of dependence are constantly evolving, control over infrastructural nodes tends to entrench the already dominant position of leading platform conglomerates.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".