Identity work in organizations and occupations: Definitions, theories, and pathways forward
Bibliographic record
Abstract
Summary Understanding how, why, and when individuals create particular self‐meanings has preoccupied scholars for decades, leading to an explosion of research on identity work. We conducted a wide‐ranging review of this literature with the aim of presenting an overarching framework that comprehensively summarizes and integrates the vast amount of recent research in this domain. Drawing on our analysis of the empirical literature, we present an enhanced conceptual understanding of identity work. We then summarize the four dominant theoretical approaches researchers have used to explain how, when, and why individuals engage in identity work. This side‐by‐side comparison of these theoretical perspectives allows us to parse out the unique contribution of each theoretical lens and highlights how these theories can be integrated into a holistic view of an inherently multifaceted concept. Lastly, we critically analyze the state of the field and lay a detailed roadmap for future researchers to draw from to expand our current understanding of how individuals work on their identities in occupations and organizations.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".