THE POWER OF LOOKING AHEAD? A FIXED-EFFECTS MODEL OF FUTURE ASPIRATIONS OVER THE LIFE COURSE AND INCOME
Bibliographic record
Abstract
Abstract Perceived life trajectories are rooted in structural systems of advantage and disadvantage, but individuals also shape their futures through setting goals and expectations. “Future aspirations” have typically been used in life course research to refer to one’s conception of their chances of success across life domains and can serve as a resource to help individuals persevere in the face of hardship. Taking a life course approach and using three waves of data from the MIDUS study, we utilize hybrid fixed effects models to assess the relationship between future aspirations and income. We find that, net of age, health, and a host of other time-varying factors, more positive future aspirations are indeed related to higher income over time, but that this relationship takes different shapes in different contexts. In particular, in lower quality neighborhoods, higher future aspirations lead to worse economic outcomes over the life course, while in higher quality neighborhoods, higher aspirations are indeed related to higher incomes. We thus argue that aspirations are only helpful in some contexts, and are inherently contextual not just in their sources but also in their effects.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".