Profiling employers involved in Apprenticeship Trailblazer groups
Bibliographic record
Abstract
This study is the first to analyse the representativeness of apprenticeship Standards Trailblazer groups. Trailblazers set the occupational profile, knowledge, skills and behaviours, and EPAs within apprenticeships. Standards take a long time to develop and representation on them requires a significant organisational commitment. \nThe analysis is based on the employment size and sector of employers represented on Trailblazer groups. \nWhilst the remit of Trailblazers is to represent employers likely to use the apprenticeship, this study has shown that not all Trailblazer groups do. Some Trailblazers have very good sectoral representation and include all of the main sectors that employ the Standard related occupations. Some do not and exclude key sectors some of which employ up to one quarter of related occupations. \nHowever, the main limitation of Trailblazer groups is their over representation of large employers, and their under representation of medium and small employers. This happens on all (for small employers) or most (for medium employers) groups. \nIt is not the case that small and medium sized employers are represented through other organisations. Very few of these organisations are sector or employer representative organisations. Most are training providers or professional/membership bodies. \nA large number of organisations are represented across all of the Trailblazers, 5,589 across the 646 Standards. We estimate that around one in ten are other organisations. Most of these are representative organisations but a similar number are training providers. \nThis study has also shown that a number of employer and other organisations sit on a large number and wide range of Trailblazers. This in itself is not an issue. It can demonstrate the commitment of employers and other organisations to apprenticeships and workforce development. However, this needs to be monitored to ensure that Standards are representative across employers that are likely to use them. \nUndertaking the analysis for this study has been challenging because the data on Trailblazer representation is not organised in any way. There is no information on the sector, employment size, type and geographical spread of organisations. Nor is there any information on the sectoral and size spread of Standard related occupations that could be used to make an assessment of the representativeness of the organisations which sit on the Trailblazers. \nSuch information should be collected as the norm for all organisations that are represented on Trailblazers. In order to assist future analysis, there should be agreement on a basic set of standardised information to be collected about organisations, so their representativeness can be assessed, along with an analysis of the employment profile of Standard related occupations. \n
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".