Bibliographic record
Abstract
Abstract The case requires students to evaluate the current situation and growth options of a small business, Dory & Nemo Early Learning Center (D&N), which provides intergenerational programming to preschool children at a retirement home. The learning center was opened in September 2014, and it was able to make a small profit each year. However, it is projected that there would be a 75 percent decrease in net income from $8,072 in Fiscal 2016 to $1,819 in Fiscal 2017. Davis and Nathans, business partners of D&N, met to discuss the following growth options: (i) Mosaic Retirement Residences' proposal to set up two learning centers at their retirement homes each year for a total of six in three years and (ii) Harmony Retirement Residences' proposal for leasing space to set up learning centers at their retirement homes, one new learning center a year for a total of three in three years. Furthermore, Davis and Nathans had decided to increase the program fee from $320 to $350 per week for the 2017–18 academic session in September. They would also like to reduce their workload from 50 to 40 hours per week, increase their vacation time from two to three weeks, and increase their salaries and bonuses. Students must consider personal objectives of business partners and mission of D&N in the analysis. They learn to identify relevant information for decision making, apply appropriate analytical tools for quantitative analysis, integrate qualitative and quantitative factors in decision making, and make recommendations consistent with analysis.
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 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) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".