Conceptualizing E-Persona through self-branding strategies on Fiverr
Bibliographic record
Abstract
Technological advancement led to the emergence of an ‘Online Labor Market’ establishing non-conventional work relationships between individuals and clients. Despite the flexibility of work, Demirel and the team identified hierarchical positioning of gig workers into the Global North and South. Furthermore, the online labor market does not provide a meritocratic space for freelancers. Conversely, freelancers in different countries encounter symbolic violence . Likewise, Gussek and Wiesche also highlight the role of algorithmic management constituting power asymmetries such as geographical, racial and gender-based discrimination of freelancers. Munoz adds further that the algorithmic material agency also deconstructs the active representation of the freelancer’s self in a digital context termed as e-persona. Consequently, a freelancer performs strategies such as self-branding to participate in the visibility games of the Online Labor Market. This study investigated the Self-branding strategies performed by Pakistani freelancers to counter the nested precarities of visibility and power asymmetries operating on Fiverr. The data was collected in the form of an asynchronous online survey. The questions were designed to attain freelancers’ demographics, opinion and behavior. The responses were analyzed in the light of Dramaturgical theory to understand self-branding as a ‘manner’ or role taken by freelancers as actors. The findings revealed five major self-branding strategies considered pivotal for consistent performance and enhanced visibility in a freelance marketplace. Furthermore, the researchers were able to identify a career trajectory for freelancers in specific domains of service. The researchers encourage intensive research on digital labor platforms for an improved understanding of the online labor market.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".