Extracting Value from Learning Curves: Integrating Theory and Practice
Bibliographic record
Abstract
Learning curves are used as management metrics in many industrial settings. However, their apparent simplicity masks the tremendous number of changes and opportunities that are summarized by this apparently simple exponential relationship. Through exploration of these underlying opportunities, practitioners are able to better identify and exploit opportunities. By integrating learning curve theory to include design, supply chain and life cycle management to a framework illustrating the complex series of events that comprise the seemingly parsimonious learning curve, it is possible to better see how learning curves reduce cost, expand existing markets and create new markets. Illustrations are used to assist practitioners in taking economic advantage of the theoretical contributions offered in this framework.
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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 | 0.022 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".