Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it