Bibliographic record
Abstract
Millions of British workers are expecting a pay rise in 2023, with nearly a quarter of respondents to a recent recruitment survey hoping for at least 10 per cent extra. It comes as nurses are negotiating for a 19.2 per cent increase, while unions recently secured 10 per cent plus a £2,000 bonus for workers at Rolls-Royce. So, as the cost of living crisis bites and the recession’s forecast to last until 2024, are union demands realistic and what’s the best strategy for squeezing a few more pounds from your boss, particularly if they have a “high ego”? Meanwhile, if you’re struggling this Christmas then please spare a thought for those hard-up bankers, who could soon receive unlimited bonuses after the Bank of England announced plans for a consultation on scrapping the so-called ‘bonus cap’. To examine how wages are looking into 2023 and tips for handling those awkward money conversations with the boss, The Leader’s joined by Dr Grace Lordan, a labour economist at the London School of Economics.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.178 | 0.113 |
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".