The role of flourishing in the STEM trajectories of emerging adults
Bibliographic record
Abstract
We focus on the use of flourishing as a new measure in studies of pathways in STEM (science, technology, engineering, and mathematical) fields. While the concept of flourishing is promising, the concept may need careful interrogation to ensure it takes structural and personal (cultural, religious, socioeconomic, and racial) differences into account. Our longitudinal study explores emerging adult’s educational and career pathways with careful attention to structural inequities, enabling us to productively explore the concept of flourishing in a larger systemic context. Drawing from a set of qualitative interviews with our participants, we explore the ways that our sample of emerging adults ( N = 30), predominantly people of color, define and discuss flourishing. The concept resonated with our diverse participants, and a substantial number did report flourishing. But despite the regularity with which the participants described experiencing racism and microaggressions, they did not often mention those harmful experiences when discussing flourishing. We caution that flourishing data on its own may provide an overly rosy image of the pathways and development, especially of young people of color. Our data suggest that it may be especially important to examine flourishing in context with other measures that can flesh out a fuller picture of well-being, especially in relation to race, racism, sexism, or any other experiences related to personal identities. In particular, instruments should be carefully designed to ensure–especially for emerging adults–that all aspects of their lives and identities can be fully understood.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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; a candidate call from one teacher head, 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".