APP IMPERIALISM: THE POLITICAL ECONOMY OF THE CANADIAN APP STORE
Bibliographic record
Abstract
In this paper, we introduce the notion of app imperialism by exploring the political economy of the Canadian iOS App Store. Building on Dal Yong Jin's concept of "platform imperialism", we argue that US companies dominate global app stores through the systematic acquisition of capital resources. App imperialism marks the outsized economic footprint and influence of US companies in national app stores. Using a longitudinal financial dataset, we qualitatively coded the top-50 of revenue-generating game apps in April 2015 and 2016. Distinguishing between value creation (generating revenue) and value capture (appropriating profit) allowed us to determine the plight of Canadian app developers. While the Canadian App Store exhibits a large degree of source diversity, featuring a high number of active app developers, we found the ability of Canadian developers to both create and capture value negligible. US owned developers, publishers, parent-organizations, and intellectual properties, on the other hand, were overrepresented. These initial findings suggest that any potential growth in the Canadian app economy will be increasingly captured by US-owned companies. These results question the effectiveness of Canadian cultural policy frameworks, which have been particularly proactive in supporting Canada-based game studios. While our initial analysis offers just a temporal and regional snapshot of the App Store's political economy, it gestures towards broader critical material issues related to platform capitalism and app diversity.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".