Recreating a Plausible Future: Combining Cultural Repertoires in Unsettled Times
Bibliographic record
Abstract
This article analyzes how young adults draw on cultural resources to understand their identities, aspirations, and goals when taken-for-granted scripts of success are perceived as less desirable or achievable. Drawing on pragmatism, we propose the concept of 'plausible futures' to capture how people rearrange elements within cultural repertoires as a practical and moral project to define their identities, aspirations, and goals. We draw on interviews with 80 college students concerning how they understand their future aspirations, including how they define personal success and broader social goals, when they face unpredictability in, and dissatisfaction with, achieving dominant meritocratic and socioeconomic ideals. We find that respondents combine elements from four cultural repertoires to work toward and envision their future: the American dream and neoliberalism, the therapeutic culture, ordinary cosmopolitanisms, and a 'Gen Z' cohort narrative. The combining of elements from each repertoire enables a hybrid set of cultural tools that hold to tenets of hard work and self-reliance while accommodating the quest for greater recognition and inclusion. We show that respondents combine cultural elements based on their ability to connect elements to futures perceived as viable and valuable.
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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".