In Cloud We Trust? Normalization of Uncertainties in Online Platform Services
Bibliographic record
Abstract
Platform-based services – services that are provided to organizations through online platforms – are increasingly being adopted and used within firms. The novelty of these services is generating significant uncertainties for both platform provider and customer organizations, but how these uncertainties are managed by the platform provider and what consequences they produce for distributed inter- organizational relationships are not well understood. I conducted an 18-month field study of a platform-based service in the enterprise cloud computing industry to examine these questions. I describe the dimensions of uncertainties associated with the platform (privacy, security, flexibility, capacity, responsiveness, innovativeness) and the platform provider (trustworthiness, credibility). I then identify four mechanisms that the platform provider enacts – controlling through code, performing algorithmic governance, producing trust rhetoric and establishing trust indicators – to manage the uncertainties. The first two mechanisms constitute platform work, while the latter two constitute trust work. Together, platform and trust work reconfigure the “arena of uncertainty” through a process of normalization, in which (a) certain dimensions of uncertainty that are unpredictable and/or cannot be managed well (e.g., responsiveness, privacy) by Sigma are downplayed, while other dimensions of uncertainty that Sigma can effectively control (e.g., security, flexibility) are emphasized; (b) value-laden “matters of concern” are objectivized into “matters of fact” through metrics, visual indicators, and algorithms. This study shows how platform firms, through a process of normalization, reconfigure the arena of uncertainty to their advantage, producing significant consequences for governing distributed inter-organizational relationships in the digital economy.
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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 | 0.019 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.018 | 0.032 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".