Lost in translation: increasing engagement between angel investors and immigrant startups in the Canadian entrepreneurial ecosystem
Bibliographic record
Abstract
Immigrant entrepreneurs are an important and growing segment of Canada’s startup scene. They contribute to the Canadian economy and lower unemployment rates by creating job opportunities through their ventures. However, they face a challenge in receiving funding from local investors in the initial stages of their startup. Angel investors are experienced professionals with an extensive network in the entrepreneurial ecosystem who specialize in early stage funding. Increasing engagement between Angels and immigrant entrepreneurs can create greater opportunities for the immigrant community in Canada and help Angels diversify their portfolio. The objective of this research is to adapt the Investment Decision Making Criteria for Angels (IDMCA) to analyze the factors that influence an Angel’s decision making process, and determine whether these factors are moderated by an entrepreneur’s immigrant status. Results from the study have shown that the Passion & Commitment (P&C), Integrity & Trust (I&T), and Open-mind & Adaptability (O&A) are important factors for an Angel’s decision making in respect to immigrant startups, whereas Industry Experience (IE), Track Record (TR), and Technology Knowledge (TK) are not important factors.
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 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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".