Curiosity signpost: Leverage alumni capital for business
Bibliographic record
Abstract
Kevin Boylan slowly pours water onto the fire. Flames rise high in the sky. The audience gasps. Boylan reminds families at the fire station open day of the dangers of a grease fire. As with many firefighters, Boylan's role affords him time for another venture, in his case, as co-founder of Firecloud365. A cloud-based fire safety software company, Firecloud365 targets public sector and hotel industry clients with large-scale building works. As a start-up, Boylan and co-founder Ryan Bradley participated in New Frontiers, an entrepreneurship acceleration programme. The co-founders received mentorship support, funding, and incubation space in the CoLab on the Letterkenny Institute of Technology campus, Boylan's alma mater. The Institute was among the company's first clients, offering Firecloud 365 credibility in the marketplace. In return, the Institute secured a fire safety system to align with its risk management portfolio. Stories like Firecloud365 show the immense potential of our alumni capital for start-up business success. The alumni-alma mater as a symbiotic relationship in action. Symbiotic indeed. A recent University of Toronto Instagram post included this attention-grabbing caption: ‘Did you know #UofT has helped over 350 start-ups scale and bring their ideas to market?’ When I fact checked this post two years later, this number had jumped to 500. Other universities can also boast this claim. The scale might be different, but the sentiment is the same: universities support business. Our alma mater provides the tools, knowledge, and support for us to allow our business acumen to shine. While entrepreneurial universities are not new, an emerging trend is the key role of the university in the local economy to foster business growth. As part of the entrepreneurial ecosystem, universities offer start-up incubation space, research support, technology transfer offices, and technology parks. They support spin-off companies and acceleration programmes, even academic courses designed specifically to meet industry requirements. Alumni Action 13: Remembering the university is a city – and a potential client Let's cast our mind back to the ‘university is a city’ concept. Companies permeate the campus: stores, cafés, restaurants, banks. Look closer. The university also has virtual subscriptions from computer software to online publications.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.138 | 0.060 |
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".