Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Drawing on quantitative and qualitative research in England, and more specifically in London, this article sheds light on trends in platform work during the COVID-19 crisis. While the number of platform workers grew, the proportion of their income it contributed to fell, making up less than a quarter of total earnings. Interviews with driving and delivery platform workers in London (Europe’s largest platform market) shed light on these puzzling trends. New recruitment by the platforms and adjustment of their algorithms during the lockdown led to downward pressure on earnings, poorer working conditions, extended waiting times, longer working hours and negative impacts on work–life balance, health and well-being. The article concludes that the pandemic provided platforms with an opportunity to consolidate their market position, but this was achieved at the cost of growing power asymmetry in the platform labour market, with workers’ attempts to organise and improve conditions undermined by over-recruitment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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 it