In Cloud We Trust? Normalization of Uncertainties in Online Platform Services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it