Funding Social Innovation in Canada: A Conversation with Stephen Huddart and Chad Lubelsky of the McConnell Foundation
Bibliographic record
Abstract
Co-editor of this issue David Peacock interviews Stephen Huddart (President and CEO) and Chad Lubelsky (Program Director) of the McConnell Foundation, a historic supporter of postsecondary education across Canada. McConnell’s investments in community service-learning, social entrepreneurial and innovation activities and social infrastructure programs and dialogues have made them a significant partner for many Canadian higher education institutions. Yet not all community-campus engagement scholars and practitioners, and Engaged Scholar readers, may have heard McConnell articulate for itself its aims and goals for Canadian higher education and society. This interview canvasses the scope of McConnell’s work and interests in community-campus engagement, and sheds light on the actions of an influential private actor in the postsecondary sector.
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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.045 | 0.015 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.020 | 0.028 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".