The political economy of Facebook’s platformization in the mobile ecosystem: Facebook Messenger as a platform instance
Bibliographic record
Abstract
Facebook's usage has reached a point that the platform's infrastructural ambitions are to be taken very seriously. To understand the company's evolution in the age of mobile media, we critically engage with the political economy of platformization. This article puts forward a conceptual framework and methodological apparatus to study Facebook's economic growth and expanding platform boundaries in the mobile ecosystem through an analysis of the Facebook Messenger app. Through financial and institutional analysis, we examine Messenger's business dimension and draw on platform studies and information systems research to survey its technical dimension. By retracing how Facebook, through Messenger, operationalizes platform power, this article attempts to bridge the gap between these various disciplines by demonstrating how platforms emerge and how their apps may evolve into platforms of their own, thereby gaining infrastructural properties. It is argued that Messenger functions as a 'platform instance' that facilitates transactions with a wide range of institutions within the boundaries of the app and far beyond.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".