A Model of Identity Crafting among Novice and Experienced Journalists in an Era of Transformation
Bibliographic record
Abstract
As numerous transformative forces - notably the prevalence of a new business logic, the emergence of new actors and the penetration of new technologies - transcend traditional newsroom practices, journalists of all ages see the cornerstones of their professional identity being disrupted. Conceiving their profession as a calling, and having different conceptions of their work depending on their generation, journalists tend to apply a variety of strategies to respond to these changes and re-craft their journalistic identity. Based on the literature on professional identity, generations and the transformation of journalism, this paper proposes a multi-dimensional model presenting the strategies of identity crafting applied by novice and experienced journalists. The model explains the differences in their strategies by juxtaposing their distinct views on calling, alongside three facets of generational identity (year of birth, life stage, nature of work). The main proposition is that novice journalists adopt a strategy of liquid identity crafting characterized by a willingness to integrate transformative elements in their work, while experienced journalists prefer a strategy of solid identity crafting, shaped by a desire to preserve traditional journalism practices. With the digital transformation of journalism as major driver, the paper contributes to the literature on professional identity with an original perspective on calling, and adds to research on generations by unpacking differences in the process of identity crafting among novice and experienced journalists.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".