One foot in the online gig economy: Coping with a splitting professional identity
Bibliographic record
Abstract
Abstract As the online gig economy diffuses into professional fields, more workers now engage in online platforms alongside traditional offline practice. How do concurrent online and offline works challenge professional identity and how do workers cope with the challenges? This study inductively explores a qualitative dataset of lawyers who worked in online platform-based and conventional offline legal services at the same time. I found that the common features of online gig work (e.g. accessibility and affordability for customers, ratings, and reviews of workers) result in contradictions with traditional legal work in terms of work content and client relations. These differences caused an emerging split in lawyers’ professional identity—the coexistence of two somewhat contradictory sub-identities. The lawyers coped with the professional identity split in one of two ways: 1) alleviating the experienced severity of the split by using the tactics of framing and distancing from online work and tailoring online work content; 2) reconciling the split by reframing professional ideals based on their new understanding of being lawyers obtained from online work. Individual differences in professional identity constructed in traditional practice were found to underlie this identity dynamic: the lawyers’ expertise specialization and customer orientation explained the strength of professional split, and those who believed that the profession is highly dynamic and will experience dramatic future changes were inclined to reconcile professional identity split.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".