Re‐looping the learning: shell's use of case studies to contribute to the company's effectiveness in external affairs practice
Bibliographic record
Abstract
Abstract Case studies are used in management teaching and executive development. They also have an important use as an aid to practice, to help practitioners learn lessons from the experience of others. For a multinational company such as Shell, case studies from the company's worldwide experience have been developed and made available to members of the company's external affairs community, so that—for example—an external affairs practitioner in the Asia‐Pacific region can learn from the experiences of practitioners in the Latin‐American region. A short description of one of the cases held in the Shell database of case studies is given. The paper concludes by suggesting how case studies should be used to improve practice, and considering some of the obstacles to the development and use of practice‐relevant case studies. Copyright © 2003 Henry Stewart Publications
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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 | 0.046 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".