Mastering Strategic Management : Evaluation and Execution, First Canadian Edition
Bibliographic record
Abstract
Mastering Strategic Management is designed to enhance student engagement in three innovative ways. The first is through visual adaptations of the key content in the book. It is well documented that many of today’s students are visual learners. To meet students’ wants and needs (and thereby create a much better teaching experience for professors), Mastering Strategic Management contains multiple graphic concept pages in ever section of every chapter of the book. Think of graphic concept pages as almost like info-graphics for key concepts in each section. This feature sets Mastering Strategic Management apart from any strategic management book on the market today. The second way the authors capture student interest through their textbook is by using a real-world company as the running example in each chapter. For example, Chapter 1 in Mastering Strategic Management utilizes Blackberry to harness the conceptual coverage of the chapter in a running, corporate, application to which students will relate. The third inventive way Mastering Strategic Management holds the attention of strategic management students is through the “strategy at the movies” feature in each chapter that links course concepts with a popular motion picture. The first Canadian Edition is an adaptation of Mastering Strategic Management. Adaptations include Canadian specific content, images and references, removal of copyright images, and inclusion of ancillary resources in the Appendix (Chapter PowerPoints).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.014 |
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".