Business Decision Making: Streamlining the Process for More Effective Results
Bibliographic record
Abstract
<p>How do executives make decisions? Are their decisions conscious or unconscious? Can they explain each decision they make? What tools can they use to improve their decision-making process? These are some of the questions this book addresses.</p><p>During the past 35 years, as an entrepreneur and senior executive of several medium-sized Canadian hi-tech businesses, the author noticed that his decision-making processes were often based either on experience or on advice received from colleagues. Seldom were the decisions based on formal or informal academic-based methods.</p><p>There is no substitute for years of experience in any human endeavor. However, tapping into some of the methods and lessons learned from personal experience can result in useful principles for others to follow. These principles are very useful, especially for entrepreneurs interested in building their businesses or executives looking for some additional help in acquiring a better decision-making mousetrap.</p>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".