Identity as career capital: enhancing employability in the creative industries and beyond
Bibliographic record
Abstract
Purpose The purpose of this paper is to broaden the conversation about the link between identity and employability by investigating how identity can function as a type of career capital. Drawing on Bourdieu’s (1990) theory of practice and Côté’s (2016) identity capital model, the authors introduce the concept of identity capitalization and elaborate on the career practices people engage in to convert identity into career capital based on studies of careers in the creative industries. Design/methodology/approach The conceptual development is based on an examination of studies of careers in the creative industries. The authors move beyond a single idiosyncratic occupational setting and offer insights about how individuals acquire, accumulate and deploy identity capital in response to varying occupational demands and institutional norms. Findings The authors identify three patterns of work – display work, authenticity work and personation work – that creative professionals use to harness identity as career capital to enhance their employability. The authors find that both the demand for authenticity and the existence of social inequalities in the creative industries present challenges for the acquisition, accumulation and deployment of identity capital. Originality/value The ability to harness one’s identity for career capital has become increasingly important for career actors in the face of a challenging labor market. This paper provides a conceptual understanding of the process of identity capitalization and presents concrete career practices in real-world settings. It also offers practical advice for individuals wishing to capitalize on their identity to maximize career opportunities.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".