Entrepreneurship in Toronto: Drivers, Barriers, and Ecosystem
Bibliographic record
Abstract
This research study investigates the entrepreneurial landscape of Toronto, particularly addressing the disproportionate focus on software and technology startups over other industries. Through a mixed-method approach involving a literature review and qualitative, in-depth, semi-structured interviews with entrepreneurs, the study uncovers a spectrum of motivations driving entrepreneurial endeavours, including a desire for autonomy, a desire to help others, a desire to learn, pursuit of passion and fulfilment, desire to meet people and financial stability. Conversely, it identifies systemic biases favouring tech startups, networking challenges, regulatory complexities, and the struggle to connect with a culturally diverse population as significant concerns among entrepreneurs. The research study examines citizens, private institutions, public institutions and government involved in the entrepreneurial ecosystem of Toronto and how they are related directly and indirectly to the entrepreneur. Stakeholders, including government bodies and grassroots communities, play crucial roles in addressing these issues. Stemming from the primary challenge of systemic biases favouring tech startups, the study introduces the #BeyondTheCode movement as a strategy for mobilising grassroots support, leveraging social media campaigns, in-person events, and letter-writing campaigns to engage decision-makers. Acknowledging limitations in scope and potential biases, the research concludes with reflections on the findings. It proposes avenues for future research, emphasising the importance of exploring systemic problem solutions beyond tech bias, such as fostering entrepreneurial connectedness for a thriving ecosystem, and the need to explore several other alternative courses of action to address the systemic problem being studied.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".