Policy Forum: Tax, Social Security, and Employment Status—Removing the Distortions in the United Kingdom
Bibliographic record
Abstract
The COVID-19 pandemic has strained tax and social security systems. Cracks that have existed for some time have been opened up further and are unlikely to close without structural repair. New insights into the shifting nature of work, combined with the development of technologies that can provide modern, practical solutions to old problems, offer the opportunity to rethink the way we tax gig workers and other non-standard providers of labour. This article argues that we need to free ourselves from the employment status classifications developed in other areas of law, for other purposes, when we consider the design of tax and social security provisions. We should aim to harmonize the tax and social security treatment of all those who provide labour as far as is practically possible in order to increase equity and remove distortions. Where that cannot be achieved, despite the benefits of new technologies, dividing lines should be dictated by tax and benefits policy objectives rather than linkages to case law that has evolved in other areas.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".