Botsourcing and outsourcing: Robot, British, Chinese, and German workers are for thinking—not feeling—jobs.
Bibliographic record
Abstract
Technological innovations have produced robots capable of jobs that, until recently, only humans could perform. The present research explores the psychology of "botsourcing"-the replacement of human jobs by robots-while examining how understanding botsourcing can inform the psychology of outsourcing-the replacement of jobs in one country by humans from other countries. We test four related hypotheses across six experiments: (1) Given people's lay theories about the capacities for cognition and emotion for robots and humans, workers will express more discomfort with botsourcing when they consider losing jobs that require emotion versus cognition; (2) people will express more comfort with botsourcing when jobs are framed as requiring cognition versus emotion; (3) people will express more comfort with botsourcing for jobs that do require emotion if robots appear to convey more emotion; and (4) people prefer to outsource cognition- versus emotion-oriented jobs to other humans who are perceived as more versus less robotic. These results have theoretical implications for understanding social cognition about both humans and nonhumans and practical implications for the increasingly botsourced and outsourced economy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".