App Imperialism: The Political Economy of the Canadian App Store
Bibliographic record
Abstract
To critically engage with the political economy of platformization, this article builds on the concepts of platform capitalism and platform imperialism to situate platforms within wider historical, economic, and spatial trajectories. To investigate if platformization leads to the geographical redistribution of capital and power, we draw on the Canadian instance of Apple’s iOS App Store as a case study. App stores are situated in a complex ecosystem of markets, infrastructures, and governance models that the disparate fields of business studies, critical political economy of communications, and platform studies have begun to catalog. Through a combination of financial and institutional analysis, we ask if Canadian game app developers are effective in generating revenue within their own national App Store. Given Canada’s vibrant game industry one would expect Canadian developers to have a sizable economic footprint in the burgeoning app economy. Our results, however, point toward the US digital dominance and, therefore, we suggest the notion of app imperialism to signal the continuation, if not reinforcement of existing instances of economic inequalities and imperialism.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".