Design Tensions in Online Freelancing Platforms: Using Speculative Participatory Design to Support Freelancers' Relationships with Clients
Bibliographic record
Abstract
This paper explores the design challenges that arise in supporting online freelancers to navigate relationships with clients. Prior studies have shown that current platform designs can lead to worker precarity in freelancer-client relationships, such as power imbalances, information asymmetry, and labor abuse. To envision alternative designs that empower workers in managing their relationships with clients, we engaged 22 Upwork freelancers in participatory speculative design activities. Through this co-design process, we identified design tensions that constrain design options as a result of conflicting values and priorities that could only be balanced and compromised rather than completely resolved. Six design tensions were identified in the context of designing for four different phases of freelancing. We observed three patterns in these tensions: 1) the freelancers' need for client involvement in their tasks and career growth, which conflicted with their skepticism that clients had sufficient incentives to be involved; 2) that there was often no viable balancing option for some tensions, but they could be addressed through changes in the platform's incentive structure; and 3) some tensions occurred not only between freelancers and clients, but also within the freelancer community. We present three approaches for addressing these design tensions and discuss how this research can support more equitable and healthy freelancer-client relationships.
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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 | 0.089 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".