Bibliographic record
Abstract
Following the U.K.'s vote to leave the European Union in the referendum of June 23, 2016, we surveyed 100 health economists regarding their opinions on "Brexit."Researchers have started to consider the challenges of Brexit (Oxford Review of Economic Policy, 2017).However, the impact on health and health services is often not directly considered.The NHS has always been a political "football", and the EU referendum campaign was no exception.The claim of "£350m-a-week" for the NHS, made during campaigning, was one of the most eye-catching elements of the Vote Leave message.However, as predicted by many commentators, this claim has been watered down following the vote to leave.With Article 50 triggered on March 29, now is a valuable time to report on those views and the challenges that Brexit may pose.Academics and policy leaders (dare we call them "experts"?)are a rich source for considering where the benefits and challenges for the NHS lie and how, potentially, we can optimise the benefits and ameliorate the impact of the challengesespecially with no historical precedent to call on (as ibid).Even though all of our respondents (59% response rate) were in favour of remaining in the EU, they identified positives and negatives, with a key focus on the labour force and the single market.
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.
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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.023 | 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".