From Trucking to Logging: How Translation Practices Shape Digital Control
Bibliographic record
Abstract
Organizations increasingly rely on digital data and algorithmic technologies to control workers. Most empirical accounts of this phenomenon focus on digital platforms, contexts where digital data such as geolocation and customer ratings effectively ‘stand in’ for reality. As a result, less is known about how digital control unfolds in contexts that require workers to engage in translation practices - situated and materially mediated efforts to represent physical labor into digital data. To investigate this issue, we conducted a 24-month ethnography of a logistics organization in the truck transportation industry. In this setting, workers such as truck drivers, dispatchers, material handlers, and customer service agents continuously engage in translation practices to ensure that flows of merchandise across space and time remain successfully aligned with digital representations. We found that actors engaged in two sets of translation practices: digitizing the physical, where physical phenomena were translated into digital data through algorithmic manipulations, and physicalizing the digital, where physical phenomena were reconstructed from digital data, often using photos and text. These practices enabled the digitization of physical phenomena across increasing spatial and temporal scales: actions, events, and work processes. In the realm of actions, control was brittle: hard in coercive measures yet prone to breakdowns. Conversely, as the scale of physical phenomena grew, control became more ductile: less coercive yet harder to break. This is because the continuous involvement in translation practices sustained the internalization of two normative expectations: a ‘continuous improvement’ mindset and constant accountability. By explaining the role of translation practices and the importance of scale in shaping digital control, this study provides two important theoretical contributions to the literature on digital control.
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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.030 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".