Frustration at Work: The Case for Subsidizing Career Switching
Bibliographic record
Abstract
Abstract Many individuals experience frustration with their work. In some cases, this is because an individual learns that the industry in which she finds herself is simply not for her, and that, if she were starting out again, she’d choose to do something very different with her time. These are instances of what economists tend to call ‘job lock’ and situations that psychologists tend to refer to in terms of individuals being ‘stuck at work’. Our aim in this paper is to explore some of the moral dimensions of this important but neglected set of cases, in which individuals feel trapped in their current line of work. Our headline conclusion is that some of the complaints to which these concerns give rise are valid and weighty ones, and that, consequently, governments have a duty to lower the social and economic costs of switching careers. Finally, we comment explicitly on the implications of our arguments for firms, arguing that, under a range of plausible circumstances, they too have a moral duty to lower the social and economic costs of switching careers, which we can conceive of as part of the demands of corporate social responsibility.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.037 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".