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Record W4391841647 · doi:10.1177/25151274241232356

Dorm-preneurship as Entrepreneurial Living and Learning: An Educational Design Ethnography

2024· article· en· W4391841647 on OpenAlexafffundabout
Ryan T. MacNeil, Santana Ochoa Briggs, Alisha E. Christie, Connor Sheehan

Bibliographic record

VenueEntrepreneurship Education and Pedagogy · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsAcadia University
FundersAcadia University
KeywordsEthnographySociologyPsychologyAnthropology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.036
GPT teacher head0.324
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes3
Has abstractyes

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