Entrepreneurship Skills Needs and Policies: Contribution to Decent Work
Bibliographic record
Abstract
Abstract This chapter explores the relationship between entrepreneurship skills and decent work (DW), and how policy can help achieve this. We review the entrepreneurship skills literature in the context of DW, highlighting the key entrepreneurship skills needed in small and medium-sized enterprises (SMEs). Thereafter, we extract lessons from selected policy initiatives in countries with broad similarities (Australia, Canada, United States and England), through the lens of DW. Our review draws on peer-reviewed journals and key United Nations and global entrepreneurship platform publications. Entrepreneurship skills deficiencies have a detrimental impact on the success and sustainability of SMEs. Yet, SME's survival and growth is currently crucial, whereby organizations need to transform in response to changing environmental, political, technological and consumer needs. This is intensified by the challenges of Covid-19, severely affecting DW and productivity. To develop and retain even a semblance of ‘decent work’, entrepreneurs need to develop appropriate skills and there is a need for suitable policy addressing this. In this chapter, we present lessons learnt based on our review and provide recommendations for entrepreneurship skills development policies aligning with DW.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".