THE EFFECT OF LEARNING ON THE MAKE/BUY DECISION
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
By including the effects of learning over time on both the production of components and their integration into complete products, we develop an engineering‐based model of outsourcing. This model provides an alternative explanation for much of what other outsourcing theories predict, as well as making several new predictions. In particular, we show that outsourcing decisions can create a path‐dependent outsourcing trap in which a firm experiences higher long‐run costs after an immediate cost benefit. We also describe conditions under which outsourcing a small fraction of component production may dominate either complete insourcing or complete outsourcing. Finally, we show that, with discounting, there is a convex, curvilinear relationship between the optimal outsourcing fraction and the rate of technological change.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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