Employment, Disabled People and Robots: What Is the Narrative in the Academic Literature and Canadian Newspapers?
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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