Social Implications of Technological Disruptions: A Transdisciplinary Cybernetics Science and Occupational Science Perspective
Bibliographic record
Abstract
In this article we argue that the disruptive social implications of skill-replacing technological innovations are determined neither by human characteristics, such as “low skills” or “low cognition,” nor by task characteristics, such as “routine,” as it is typically assumed in the predominant economics and management science literature, but by the cybernetic characteristics of the innovations. We also propose that the negative effects of technological disruptions on human well-being cannot be fully understood without the use of a transdisciplinary approach involving cybernetics science and occupational science, and that it is urgent that policymakers look beyond their narrow effects on productivity and on the labor force, and consider instead the complexity of the interactions between cybernetic technologies and meaningful human occupations. We offer as an example the case of the fast adoption of online food delivery services and of remote work technologies during the COVID-19 pandemic. Ethical implications are derived from the arguments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".