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
Product platforming—a specific approach to new product development utilizing common technology components or subsystems deployed across multiple products or product lines—has been argued to bring numerous valuable organizational outcomes (e.g., effectiveness of R&D process, superior postlaunch product commercial performance, and ultimately sustained competitive advantage). Yet, large‐scale longitudinal empirical examinations of the mechanisms linking product platforming to firm performance are scarce. Drawing on the concepts of architectural leverage and product life cycle flexibility, the article presents the development and empirical test of a set of hypotheses regarding the commercial outcomes of platforming at the product level using a unique dataset comprising all products developed and sold by a large, global LED lighting manufacturer in 2010–2015. The results suggest that platformed products demonstrate significantly higher sales and gross profit margins aggregated over their product life cycle (PLC), vis‐à‐vis the comparable group of nonplatformed, individually developed products. In addition, the findings demonstrate that a product platforming development approach appears to extend the PLC relative to nonplatformed products based on an integral, nonmodular product architecture.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 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