Exploring equity crowdfunding potential in Newfoundland and Labrador
Bibliographic record
Abstract
The study adopted a place-based approach to evaluate the potential of Newfoundland and Labrador (NL) founders in terms of human and social capital, innovation, and active knowledge sharing to attract investors through Equity Crowdfunding (ECF). A systematic literature review was conducted to understand the relevant factors for ECF success, and primary data was gathered through a survey of small tech-based enterprises to understand whether of NL founders and their companies possessed these ECF success factors. Additionally, observations of founders' and companies' social media and websites provided further data. The findings highlight the founders' strengths and areas for improvement, offering insights into their readiness for ECF success. Additionally, the study suggested initiatives that the policymakers in NL might consider to make ECF a feasible fundraising platform for NL founders. By examining regions that differ culturally and economically from large urban areas, the study contributes valuable perspectives to the ECF literature, which predominantly focuses on mainstream regions and platforms. Given the emerging role of ECF in Canada as an alternative fundraising method, the study's findings may hold significant implications for policymakers and other relevant stakeholders.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".