Bibliographic record
Abstract
Social implications This case presents some of the entrepreneurial challenges faced by a female leader in the technology sector who conceived a new product based on her passion to help others especially those most disadvantaged. Learning outcomes Upon completion of this case study, students should be able to prepare supply chain and distribution analysis that considers ethics and sustainability, integrate philanthropic efforts as part of an organizational strategy and recognize strategies to promote equity within and beyond an organization. Case overview/synopsis Connie Stacey (she/her) is an entrepreneur and president of Growing Greener Innovations, an award-winning battery energy storage company based in Alberta, Canada, with a mission to end energy poverty globally. With the emergence of COVID-19 as a global pandemic in 2020, Stacey turned her attention to an innovation called Project Rescue, a ventilator that uses non-identifying patient vitals to track data. It serves as a pandemic early warning system, addressing two key challenges: pandemic data are prone to error, and real-time information is non-existent after the pandemic has spread. This new product was conceived based on her passion to help others, especially those most disadvantaged. This multi-faceted case focuses on the many challenges that Stacey and her team needed to address. The dilemma in this case centres on establishing supply chains amid a pandemic, as well as prioritizing the corporate social responsibility elements of philanthropy and equity within her organization (and beyond). Complexity academic level This case is appropriate for third- or fourth-year undergraduate or graduate-level students. Supplementary materials In addition to “call out boxes” throughout the case and teaching note, additional readings/links/videos are outlined below. (These supplementary materials, “Teaching Tips”, are included in the teaching notes as well.) Subject code CCS 11: Strategy.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".