Bibliographic record
Abstract
Social implications The case study will help improve systemic gender-related challenges for women in STEM, male-dominated nontraditional workplaces, such as mining, and contribute to CASE FOR WOMEN database of women-centered business teaching cases. Learning outcomes The learning objectives are as follows: discuss gender issues in nontraditional science, technology, engineering, mathematics (STEM)–related male-dominated industries; conduct a strategic competitive strengths and weaknesses, the opportunities and threats analysis and political, economic, social, technological, legal and environmental analysis; evaluate relevant information and decision criteria to assess the options; provide recommendations for strengthening vision mission and strategy; and analyze the business model using the Business Model Canvas. Case overview/Synopsis Alicia Woods (she/her), founder of Covergalls Inc., was facing an unexpected challenge during the COVID-19 worldwide pandemic restrictions and lockdowns which had created an unprecedented disruption to her business. Should Covergalls continue on its current path, or was it time to branch out? Complexity academic level This case is suitable for diversity, equality and inclusion, strategic management, entrepreneurship, marketing or leadership courses at the undergraduate BBA and graduate MBA level on campus or online. Supplementary materials Teaching notes are available for educators only. Subject code CCS 3: Entrepreneurship.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.779 | 0.387 |
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".