Empowering Vulnerable Populations Through Transformative Approaches and Research
Bibliographic record
Abstract
Two years ago, HEC Montréal launched the result of numerous consultations that led to updating its mission: to building on our excellence in teaching and research. HEC Montréal is a French-language institution open to the world and solidly rooted in Quebec society, training management leaders who make a responsible contribution to the success of organisations and to sustainable social development. HEC Montréal’s renewed mission echoes the willingness of faculty members to rethink business practices to make them more sustainable and more inclusive. The Scaling Entrepreneurship for Economic Development (SEED) project is a case study within HEC Montréal’s research ecosystem led by our Social Impact Hub, IDEOS, that illustrates how rethinking research methods and collaborations across sectors and across cultures can amplify opportunities for the economic empowerment of vulnerable populations. The goal of SEED is to create a network of international and local promoters of entrepreneurship programmes, as well as international and local researchers with expertise in entrepreneurial scaling.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".