Maritime Bus: Goal setting and surviving the pandemic crisis [Case study and teaching notes]
Bibliographic record
Abstract
Maritime Bus is a case study focusing on entrepreneurship; strategy; goal setting and decision-making during the global pandemic crisis. The entrepreneuer in this case is faced with navigating a busing company in Atlantic Canada through declining ridership, mounting financial pressures and contemplating laying off staff. This case will certainly challenge and motivate your undergraduate or graduate students. \n \nThe teaching note outlines the corresponding "Maritime Bus Goal Setting and Surviving the Pandemic Crisis." The purpose of a case study teaching note is to provide educators with a comprehensive and detailed guide on how to effectively use a specific case study in the classroom. It serves as a roadmap for instructors, offering insights into the case's objectives, key themes, and potential teaching strategies. The teaching note outlines the case's background information, identifies important teaching points, and suggests discussion questions, analysis frameworks, and recommended resources. It also assists instructors in managing classroom dynamics, addressing potential challenges, and facilitating meaningful student engagement. Ultimately, the teaching note aims to enhance the learning experience by empowering educators to effectively navigate and leverage the case study material to promote critical thinking, problem-solving, and application of concepts.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".