The Divergent Mental Health Effects of Dashed Expectations and Unfulfilled Aspirations: Evidence from American Lawyers’ Careers
Bibliographic record
Abstract
Considerable work has shown that optimistic future orientations can be a resource for resilience across individuals’ lives. At the same time, research has shown little downside to “shooting for the stars” and failing. Here, we bring these competing insights to the study of lawyers’ careers, investigating the relationship between mental health and failure in achieving desired career advancement. To do this, we differentiate between expectations and aspirations for the future, a conceptual distinction that has been much theorized but little tested. Using longitudinal data, we show that dashed expectations of making partner are associated with depreciated mental health outcomes, whereas a similar relationship does not exist for unfulfilled aspirations. We conclude that inasmuch as expectations are more deeply rooted in an individual’s realistic sense of their future self, failing to achieve what is expected is more psychologically damaging than failing to achieve what is simply aspired. Our findings contrast with studies of younger people that demonstrate fewer consequences for unfulfilled future orientations, and so we highlight the importance of specifying how particular future-oriented beliefs fit into distinct career and life course trajectories, for better or for worse. In the process, we contribute to the academic literatures on future orientations, work, and mental health.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".