Employment, Disabled People and Robots: What Is the Narrative in the Academic Literature and Canadian Newspapers?
Bibliographic record
Abstract
The impact of robots on employment is discussed extensively, for example, within the academic literature and the public domain. Disabled people are known to have problems obtaining employment. The purpose of this study was to analyze how robots were engaged with in relation to the employment situation of disabled people within the academic literature present in the academic databases EBSCO All—an umbrella database that consists of over 70 other databases, Scopus, Science Direct and Web of Science and within n = 300 Canadian newspapers present in the Canadian Newsstand Complete ProQuest database. The study focuses in particular on whether the literature covered engaged with the themes of robots impacting (a) disabled people obtaining employment; (b) disabled people losing employment; (c) robots helping so called abled bodied people in their job to help disabled people; or (d) robots as coworkers of disabled people. The study found that robots were rarely mentioned in relation to the employment situation of disabled people. If they were mentioned the focus was on robots enhancing the employability of disabled people or helping so called abled-bodied people working with disabled clients. Not one article could be found that thematized the potential negative impact of robots on the employability situation of disabled people or the relationship of disabled people and robots as co-workers. The finding of the study is problematic given the already negative employability situation disabled people face.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.016 | 0.028 |
| Science and technology studies | 0.027 | 0.017 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".