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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it