Strategic management of technological learning : learning to learn and learning to learn-how-to-learn as drivers of strategic choice and firm performance in global, technology-driven markets
Bibliographic record
Abstract
INTRODUCTION The Concept of Decision Under Uncertainty OVERVIEW OF DECISION AND STRATEGY MAKING SCHOOLS The Analytical or Synoptic School of Decision Making The Experiential or Incremental School of Decision Making The Design School of Strategy The School of Strategy The Emergent Learning and Deliberate Planning or Austrian School of Strategy THE CONCEPT OF PARADIGM IN DECISION MAKING The Analytic Paradigm The Cybernetic Paradigm The Cognitive Paradigm THE CONCEPTS OF CULTURE, FEEDBACK, AND LEARNING IN DECISION MAKING AND STRATEGY CRAFTING Culture as a Medium for Learning Feedback as a Tool for Learning Learning: Autonomy and Responsibility STUDY METHODOLOGY Empirical Evidence TRANSPORT MANUFACTURING SECTOR CASE STUDIES Industry Overview Bayerische Motoren Werke AG Daimler-Benz AG Matra Automobile Airbus Industrie PROCESS SECTOR CASE STUDIES Industry Overview Bristol Myers Squibb Miles/Bayer Corp. Compagnie de Saint Gobain SA ELECTRIC POWER GENERATION SECTOR CASE STUDIES Industry Overview Consolidated Edison Duke Power Corporation Rochester Gas and Electric Tennessee Valley Authority Ontario Hydro Electricite de France SYNTHESIS OF THEORETICAL AND EMPIRICAL EVIDENCE Towards an Organizational Architecture of Technological Learning Building Sustainable Competitive Advantage Based on Learning Technology Transfer and Technological Innovation The Meta-Cognitive Paradigm of Decision Making Strategic or Active Incrementalism Empirically Identified Instances of Technological Learning, Meta-Learning and Un-learning in the Organizations Studied CONCLUSIONS AND RECOMMENDATIONS Further Research on Technological Learning APPENDIX REFERENCES
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".