Using New Planned Pedagogical Scaffolds to Build Value Creation Entrepreneurial Mindsets
Bibliographic record
Abstract
Entrepreneurship educators seek to develop entrepreneurial mindsets among learners, but it is not clear what constitutes an entrepreneurial mindset or how to develop one. Planned pedagogical scaffolds have been identified as being a key means to build effective entrepreneurial mindsets through the acquisition of knowledge chunks gained from experiential learning. However, it is not clear what knowledge chunks are required, how to assemble them, or why. In this conceptual paper, we draw on transaction cognition entrepreneurship theory to create a series of planned pedagogical scaffolds, consisting of both activity and coaching elements that help learners develop an expert value creation mindset. Using this theory, we explain what needs to be learned (specific knowledge chunks), how these knowledge chunks can be aggregated into knowledge structures (through a novel template), and why novel activities and coaching tied to their application can build expert value-creation mindsets. In doing so, we respond to calls to better understand entrepreneurial mindsets and calls to develop pedagogy to develop such mindsets. We provide entrepreneurship educators with a new and much needed (integrating) mechanism that illuminates the content of one aspect of an entrepreneurial mindset, specific direction to educators on how they can better assist learners build such mindsets, and a theory-based explanation of why this approach is effective.
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.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".