Bibliographic record
Abstract
Purpose PwC is currently working with a broad cross‐section of employers in the UK to create a new Higher Apprenticeship for the professional services. The purpose of this paper is to explore the environment and drivers for the creation of the new Higher Apprenticeship framework, the work PwC is leading to develop it and the outlook for Higher Apprenticeships in the professions. Design/methodology/approach The information provided in this case study is drawn from the organisation's own work in creating a new Higher Apprenticeship Framework. It expands on research undertaken by PwC. Findings Creating a skilled workforce is consistently the number one priority for CEOs worldwide. Whilst graduate recruitment has been the long established route into professions such as accountancy, consulting and law, employers are looking to offer a wider range of different entry routes that enable them to attract and recruit from a broader, more diverse talent pool. Originality/value Employers are now playing a more active role in the design and delivery of programmes that will provide them with the pipeline of skilled people they need. The paper highlights how the higher apprenticeship currently in development will respond to these needs and how PwC propose to progress this further.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.027 | 0.026 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.003 | 0.043 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.049 | 0.012 |
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".