Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it