Dorm-preneurship as Entrepreneurial Living and Learning: An Educational Design Ethnography
Bibliographic record
Abstract
Entrepreneurship residence halls/dorms have been appearing on more and more campuses, especially in the United States and Canada. However, there is a very thin knowledge base on which to construct and design these expensive campus facilities/programs. Sometimes called “dormcubators,” these facilities/programs are linked to both the university business incubation (UBI) and living-learning communities (LLC) movements. As a result, the design and delivery of these hybrid spaces/programs can be oriented toward achieving economic (i.e., starting companies), social (i.e., building communities), and/or educational (i.e., entrepreneurial learning) outcomes. Prior research on other kinds of post-secondary LLCs suggests that the intended outcomes are also likely accompanied by unintended negative consequences for students and faculty. To understand how various dorm-preneurship program designs have worked in practice, this paper applies an ‘educational design ethnography’ approach to four different residential entrepreneurship programs at the University of Waterloo, Canada. The key finding is that problems arise when dorm-preneurship programs lack any link to educational/curricular outcomes and focus only on economic objectives or social ones. Four design principles are developed to guide research and development of similar programs in other contexts.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".