Dataset of worker perceptions of workforce robotics regarding safety, independence, job security, and privacy
Bibliographic record
Abstract
With an aging blue-collar workforce spanning critical sectors such as construction, transportation and delivery, manufacturing, and warehousing, there is an increased need for collaborative workforce robots. Workers in these sectors are prone to lifting related workplace injuries that lead to a reduction in job longevity. Fear and perception of robotics is influenced by a broad range of demographic and socio-economic factors. In this paper we present a dataset consisting of 337 complete responses to a 40-question survey that we administered anonymously via Google Forms to blue-collar workers in Australia, Canada, United Kingdom, and United States of America working in six different job sectors, namely manufacturing, retail, transportation & delivery, warehousing, construction, and contract work. The questions range from worker demographics (7 questions), perceptions toward physical safety in the workplace (8 questions), perceptions toward working with human coworkers in the workplace (6 questions), perceptions toward working with robots (16 questions), and perceptions toward data privacy on robots (3 questions). The dataset will enable research on understanding worker concerns with sensing systems and data privacy in workforce robots and enable data informed recommendations on privacy and security preserving sensing systems on existing and future robots. The dataset will enable researchers to understand how workers perceive of robots of varying capabilities with regards to the worker's own perceptions of safety, independence, and job security. Researchers can also use the dataset to understand how barriers to safety in the workplace influence worker perceptions and understand how the existing blue-collar workforce views collaborations with other workers and robots. The dataset can also enable researchers to understand how perceptions of robotics is influenced by demographic factors, country of work, job sector, and workplace location.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".