ANALYZING PLATFORM POWER: APP STORES AS INFRASTRUCTURAL PLATFORM SERVICES
Bibliographic record
Abstract
This paper examines how platform power is operationalized in the specific case of the iOS App Store. We take a first step in developing an analytical framework that critically examines the infrastructural power relations that constitute online platform ecosystems. Building on a relational understanding of power, we propose an analytical vocabulary to systematically interrogate the material power relations among the three main actors active in platform ecosystems: platform operators (e.g. Apple), third party institutions (e.g. app developers, businesses, governments), and end-users (i.e. individuals). To better differentiate among these three different actors in platform ecosystems, the paper proposes to study platform power at five expanding levels, similar to those of ecological ecosystems: individual actors, infrastructural platform services, company platform ecosystems, geopolitical platform ecosystems, and the global platform ecosystem. Studying infrastructural platform services, such as app stores, offers relevant insight into how globally operating platforms are able to set, steer, and bend rules and norms that impact individual actors on the local and national level. In the case of app stores, the paper shows that platform power is not casual or discursive, but highly strategic, uniform, and centralized. By interrogating the operationalization of platform power at the platform service level, the paper demonstrates that platform power is not a property of one platform itself, but a corollary of a platform’s function in the context of other platforms and actors in a dynamic 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".