Platformization’s Elsewheres: Japanese Convenience Stores and the Platform Economy
Bibliographic record
Abstract
Platformization’s elsewheres refers to other locations and places where platformization as a process takes place. This article focuses on the franchised Japanese convenience store as a particularly salient site from which to understand platformization in Japan. It is also crucial for thinking the platform economy historically and regionally within Asia where Japanese-style convenience stores abound, as well as globally given how Japan’s convenience stores were a model of the internet-connected mobile phone that in turn becomes a model for iPhone and Android smartphones. Focusing on the convenience store and its Japanese trajectory of development allows us to see the process of platformization of the franchised, networked, logistically-enabled convenience store from the 1970s to the present. The convenience store is, I argue, a crucial, if overlooked, site for platformization in Asia and beyond. It is also a key site for rethinking the most central of feelings to the platform economy: convenience.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".