Role of mentors in developing the social competencies (SC) of their protégée-entrepreneurs (PE) in high-tech incubators (HTI)
Bibliographic record
Abstract
High-tech incubators offer their entrepreneurs mentoring services to help them achieve goals faster. In a successful mentoring relationship protégées learn from the statements, actions, questions, and communication styles of their mentors. Mentors can play an important role in developing their protégées’ social competencies, which allow them to increase their social capital. This research tests a predictive model for the contribution of mentors to the development of their protégées’ social competencies in a high-tech incubation environment. The predictor variables of the model are the active communication-time between mentors and their protégée entrepreneurs, and the age of a mentoring relationship, referred to as elapse-time. The outcome variable is the development of social competencies of protégée-entrepreneurs. Moreover, the levels of trust from protégée-entrepreneurs towards their mentors might moderate this time social competency relationship. The social competencies of individuals involve six elements: emotional expressivity, emotional sensitivity, emotional control, social expressivity, social sensitivity, and social control. The Social Skills Inventory (SSI), an established psychometric scale that captures all six dimensions of social competencies, is used to test this model. After the participation of 99 protégées entrepreneurs from 10 incubators at Ryerson University, a new seven-item trust scale has been validated; however, the roles of elapse-time and communication-time in developing the social competencies of protégée-entrepreneurs are not supported. Surprisingly, after the verification of the SSI, it turned out that it is not valid to the participating sample set. In conclusion, despite the claimed generalizability of the SSI, it is now questionable, and the creation of a social competency scale for incubated entrepreneurs is an opportunity for future research.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".