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
Although imitation is more abundant and prevalent than innovation in firms’ product and process development activities, it has been understudied in research on innovation and R&D management. For example, a valid and reliable objective firm‐level measure of the intensity of imitation activity is lacking in the extant literature. This measure is necessary to understand the antecedents and consequences of firms’ imitation activity, which has implications for R&D management. In this paper, we present novel methods that employ patent infringement litigations data to improve on the validity and reliability of measuring firms’ imitation activity. We validate our proposed measure by presenting a first model and test of R&D as a multiple‐output production function with R&D expenditure as the primary input, and innovation and imitation as joint outputs. This is in contrast to current R&D models as a single‐output production function of either innovation or imitation. This study uses a sample of 227 public firms from the computer, semiconductor, and pharmaceutical industries in the United States during 1991–2010.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it