Street Challenge Pedagogy: How Walking Down Main Street Broadens Entrepreneurship and Ecosystem Perspectives
Bibliographic record
Abstract
Street Challenge is a community engaged, place-based, experiential learning pedagogical framework that heightens students’ understanding of the ecosystems entrepreneurs work within. Entrepreneurship courses often focus on students as future entrepreneurs, taking the perspective of business owners as independent agents. Ecosystem awareness, however, allows students to develop broader perspectives about entrepreneurs and their own goals by adding a broader context. We present an overview of several exercises and projects used to explore the facets of a business district, which we use as an example and an analogy of business or community ecosystems. Implementation of Street Challenge in different locations, courses, and modalities demonstrated that the method can be adapted and customized to fit a variety of entrepreneurship education needs and intended learning outcomes. Using local neighborhoods as tangible contexts for teaching entrepreneurship within ecosystems, as well as primary research and effective communication skills, is highly effective. Equipping students with perspectives and conceptual frameworks to address future career situations as self-employed professionals or entrepreneurs is a worthwhile endeavor in itself; with Street Challenge students also discover the value of civic engagement and a sense of agency in addressing ecosystem or community challenges.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".