Bibliographic record
Abstract
This paper offers an analytical framework to critically examine the power relations that structure the online platform ecosystem. Following a relational understanding of power, it focuses on the connections between the five leading platform corporations - Alphabet-Google, Apple, Facebook, Amazon, and Microsoft (GAFAM) - and the many other digital properties (i.e. platforms, websites, and apps) that populate the online ecosystem. Exploring these connections, we notice that a growing number of digital properties are integrated with, and increasingly dependent on the infrastructural services offered by the GAFAM platforms. These services include: advertising networks, login services, cloud hosting, app stores, payment systems, data analytics, video hosting, geospatial and navigation services, search functionalities, and operating systems. Such infrastructural services allow a wide variety of companies to make their products and services available online, attract and target users, analyze their activities, and generate revenue. It is through the ubiquitous integration and consistent use of these infrastructural services that platform power emerges and is consolidated. To demonstrate how such power relations can be analyzed, the paper highlights two key infrastructural services: app stores and ad networks. For each service it discusses two levels of analysis that can be pursued to gain insight in the workings of platform power. Ultimately a systematically analysis of the key infrastructural services will need to be developed to arrive at a refined taxonomy of platform power relations. Such taxonomy is essential to establish guidelines for governing the platform ecosystem in correspondence with key public values.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".