Understanding Equity, Diversity and Inclusion in Business Incubators
Bibliographic record
Abstract
This research seeks to understand how incubators support equity, diversity, and inclusion (EDI) with the businesses that reach out to them. Qualitative research was used in this study by interviewing 5 participants online via WebEx. All participants work at a business incubator and gave a better understanding of what their own incubator does to support EDI. Participants were able to determine ways they support EDI in their responses such as bursaries offered, workshops, makeup of staff/team and mentorship. Participants were able to identify what their exact role is at their organization and what they personally do to help the businesses that approach their incubators feel supported by using an EDI lens. The incubators and participants interviewed are placed all over Ontario which has given a diverse set of responses on how involved EDI is at the incubator. Participants noted that EDI is an important topic of discussion for their businesses as they come from marginalized backgrounds and have their own personal story on why they want to take steps to ensure EDI for the future of their organization.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.013 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".