From Synchronizing to Harmonizing: The Process of Authenticating Multiple Work Identities
Bibliographic record
Abstract
To understand how people cultivate and sustain authenticity in multiple, often shifting, work roles, we analyze qualitative data gathered over five years from a sample of 48 plural careerists—people who choose to simultaneously hold and identify with multiple jobs. We find that people with multiple work identities struggle with being, feeling, and seeming authentic both to their contextualized work roles and to their broader work selves. Further, practices developed to cope with these struggles change over time, suggesting a two-phase emergent process of authentication in which people first synchronize their individual work role identities and then progress toward harmonizing a more general work self. This study challenges the notion that consistency is the core of authenticity, demonstrating that for people with multiple valued identities, authenticity is not about being true to one identity across time and contexts, but instead involves creating and holding cognitive and social space for several true versions of oneself that may change over time. It suggests that authentication is the emergent, socially constructed process of both determining who one is and helping others see who one is.
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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 | 0.030 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.002 | 0.004 |
| 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".