Evaluating the use of action learning sets to facilitate \ncontinuing professional development: a pilot study \nwith entrepreneurs and SME owners in North East \nEngland
Bibliographic record
Abstract
The importance of SMEs to the UK economy is overwhelming. Organizations employing less than 250 people make up 99.8% of companies and account for 52% of turnover and 55.6% employment in the private sector (DTI, 2003). Within current economic conditions, it is imperative that the SME and entrepreneurship community is prepared for the future and is equipped to survive in a recession and develop their business thereafter. \nThere has been a focus in recent SME literature on management and leadership development(Morrison, 2003; Perren and Grant, 2001).However, SME owners and entrepreneurs might derive more benefit from a programme specifically tailored to their needs and stage of growth. \n \nResearch design and methods of data collection and analysis or method of inquiry: \nNewcastle Business School, funded by the Northern Leadership Academy, undertook a pilot study of North Eastern SMEs and their development needs. The research lasted six months during 2008 and adopted an Action Learning approach. Alongside a small group of peers, Action Learning establishes a link between reflecting on past events, making sense of actions and identifying new behaviours. Membership of a small group or ‘set’ provides participants with dedicated time and space to attend to this relationship between reflection and action (Brockbank and McGill 2006). \n20 SME owner managers from varied backgrounds (including car dealership, PR, cleaning and media) were recruited to the pilot study. Initially, they met the facilitator to discuss needs, undertake some professional development and share experience. From this, a programme of CPD was developed for subsequent sessions. These were documented, and the data used to analyse learning needs of the group. \n \nMain findings: \nIt was immediately apparent that the SME owner managers shared generic educational needs, including: \n· Better knowledge of potential uses of IT and where to find reliable local support.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".