Securing Funds for the Proposed NHS Multi-year Funding: The Feasibility of Using a Hypothecated Tax
Bibliographic record
Abstract
In June 2018 the UK Government announced an increase of 3.4% per annum in spending on the NHS in England for each of three years. It indicated that taxes will rise to pay for this. Debate has increased as to whether a separate (hypothecated) tax should be introduced to fund the NHS. The Kings Fund has published this [year]( https://www.kingsfund.org.uk/publications/hypothecated-funding-health-and-social-care) on the topic. OHE published an academic review sometime [ago]( https://www.ohe.org/publications/hypothecated-health-taxes-evaluation-recent-proposals). This OHE publication by Nick Timmins, a senior fellow at the [Institute for Government]( https://www.instituteforgovernment.org.uk/person/nicholas-timmins) and the [Kings Fund]( https://www.kingsfund.org.uk/about-us/whos-who/nicholas-timmins), sets out arguments in favour and against. It is based on the seminar he gave at OHE in July 2018. The issues he explores include - - The argument that hypothecation will bring greater stability, transparency and public support; - Which tax(es) might be suitable? - Possible distortions in public expenditure that might result; - Dealing with shortfalls and surpluses; - UK experiences of revenue hypothecation in the past, distinguishing soft (non-binding) approaches such as Vehicle Excise Duty, and hard (binding) pots such as National Insurance, neither of which have held. He concludes by quoting a John Appleby [blog]( https://blogs.bmj.com/bmj/2018/03/29/john-appleby-a-dedicated-tax-to-fund-the-nhs-a-zombie-policy-idea/)which argues that the key decision is to agree (or not) to spend more money on the NHS. Creating a new tax is a distraction from this.
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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 | 0.114 | 0.264 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".