When digital platforms enter informal sectors: work formalization and institutional change
Bibliographic record
Abstract
• When entering informal sectors, digital platforms formalize work practices to make them amenable to the platform model. • To enable formalization, platforms engage in institutional changes to alter the sector’s dominant logic. • Platform-enabled formalization involves codifying interactions, standardizing practices, and controlling boundaries. • Platforms shift informal sectors from an informal market logic to a matchmaking logic, then to a service system logic. • The design and governance of digital platforms for informal sectors need to account for the effects of formalization processes. Digital platforms are undermining long-standing formal institutions for the organization of work. However, when they enter informal sectors, they contribute to the opposite effect by increasing the formalization of work activities. In this study, we investigate this hitherto unexamined phenomenon by drawing on a case study of Gojek, one of the largest digital platforms in Southeast Asia. We identify three main mechanisms through which the platform transformed work in an informal transportation sector to make it amenable to integration into their platform model: codifying market interactions, standardizing work practices, and controlling ecosystem boundaries. We develop an understanding of the institutional changes that supported the platform-enabled formalization by noting the shifts in the sector’s dominant institutional logic from an informal market logic to a matchmaking logic, then to a service system logic. We discuss the implications of these institutional changes for the platform and the workers.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".