Romanticisation and monetisation of the digital nomad lifestyle: The role played by online narratives in shaping professional identity work
Bibliographic record
Abstract
Some occupations are subject to more complex identity work processes than others. This rings true for those professional endeavours that are relatively poorly known and that cannot rely on institutions as a reference for identification, such as digital nomadism. Digital nomads can broadly be defined as professionals who embrace extreme forms of mobile work to combine their interest in travel with the possibility to work remotely. Building on a two-stage data collection process, this paper proposes a typology that characterises four archetypes of digital nomad lifestyle promoters’ narratives found online and show how these online narratives play a role in the process of identity work of other digital nomads. Our contributions are two-fold. First, we show that while the archetypes act as an important online identity regulatory force, they do so through dis-identification. Second, we explain how identity work for digital nomads involves evaluating discursively available subjectivities and propose a three-step reflexive process that entails (i) interpreting, (ii) dis-identifying and (iii) contextualising. We contend that our findings extend beyond the specific case of digital nomads and shed light onto the intricacies of work identity for ‘new’ occupations that are romanticised and monetised through social media and beyond.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".