The food delivery industry and its lack of care in gender equality: the speculative case of ‘GiGi’
Bibliographic record
Abstract
This research takes a speculative design-led approach to increase care and safety for the women working in the 'Gig Economy', specifically the food delivery industry.Past research analysing the employment conditions of the Gig Economy have identified unsafe practices that particularly affect female food delivery drivers operating in large cities.Among others, women have no safe access to wash areas, or no possibility to choose safer routes.More recent research put forward regulations or worker unionisation as possible solutions to address some of these problems.Our strategy, based on 'Research through Design' (RtD), envisions the increase of inclusivity, safety, and care by designing a platform -'GiGi' (the author's word game of 'Gig Economy Gigs') -that empowers women through training and professional development.With 'GiGi' we combined technology, service design and business to explore how design practices could increase the level of care for women by developing a 'caring transformation' for the food Gig Economy; through the 'GiGi' physical and digital community hub (self-) employed women can reimagine and redesign their own working conditions beyond its current conditions and limitations.We report on our methods to discuss what implementation a prototype should require to effectively design care through participatory co-creation practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".