Digitalized Drones in the Steel Industry: The Social Shaping of Technology
Bibliographic record
Abstract
New digital technologies are often framed as an inevitable and determining force that presents the risk of technological unemployment and the end of work (Lloyd and Payne, 2019). In manufacturing specifically, digitalization is referred to asIndustry 4.0, a term that emerged in Germany as a central economic and industrial policy and has taken on a wider resonance across Europe (Pfeiffer, 2017). In this article, we explore the workplace implications of a specificIndustry 4.0innovation. We examine the insertion of drone technology—as a timely and topical example of industrial digital technological innovation—in the steel industry. The article brings to debates on the digital workplace a discussion of the relationship between the material forces of production and the social relations within which they are embedded (Edwards and Ramirez, 2016). Drawing on interview data from two European industrial sites, we suggest that the increasing use of drones is likely to be complicated by a number of social, economic and legal factors, the effects of which are, at best, extremely difficult to predict. Introduced for their potential as labour-saving devices, drones seemingly offer a safer and more efficient way of checking for defects in remote or inaccessible areas. However, whilst employers might imagine that digital technologies, like drones, might substitute, replace, or intensify labour, the workplace realities described by our interviewees make insertion highly contingent. We highlight several such contingencies, with examples of the ways that the steelworkers’ interests differ from those of their employers, to discuss how the insertion of digital technologies will ultimately be shaped by the power, interests, values and visions prevailing in the workplace, as well as in the wider polity and public culture.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".