Seeking authenticity in diverse contexts: How identities and environments constrain “free” choice
Bibliographic record
Abstract
Abstract Diversity and inclusion are a key goal in 21st century society, but people continue to self‐segregate in occupations, communities, and everyday interactions. Are people's choices to separate by groups into these different spaces truly “free?” In this paper, we review and extend a new framework for understanding how social identities contextually and automatically constrain the choices people make. We consider how situations subtly cue a sense of fit to one's identity, automatically eliciting state authenticity and a desire to return to those settings that afford authenticity and avoid those that do not. Actors and observers alike often explain these behaviors after the fact as freely chosen. We discuss how the SAFE model can clarify and expand what it means to feel a sense of belonging and explain why those who are advantaged in a setting are often less aware of the way in which their identity advantages them. We end by highlighting how environments can be shaped to foster fit and authenticity among members of underrepresented groups as a means to facilitate diversity.
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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 | 0.001 | 0.002 |
| 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".