A Critical Analysis of the Small Business and Startup Community in the U.S. State of Maine: A Sustainable Way Forward
Bibliographic record
Abstract
This study is a student-faculty research collaboration that examines the landscape in which small business owners, startups and entrepreneurs operate within the U.S. State of Maine with the aim of bringing invaluable insight and creating awareness. Supporting small business is a salient topic for local economic development councils, governments, and agencies. A survey was conducted and distributed to small business owners and entrepreneurs to gather their perspectives and opinions. One finding is that there are significant gaps in awareness with regards to support entities and their associated programs in general. Another finding is that survivorship bias has a large presence in the data collected previously by other agencies as well as by the data collected by the study. Finally, sustainable recommendations are suggested which, when implemented by the state of Maine as well as by municipalities, counties, or regional development boards, governments, and business support organizations, will increase the efficacy of existing policy and create a more cohesive, diverse, equitable, inclusive, resilient, and robust small business, startup, and entrepreneurship community.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".