SHORT-TERM AND LONG-TERM RETURNS TO INNOVATION FROM THE APPLICATION OF TECHNOLOGY AND TRAINING PRACTICES
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
The intention of this paper is to investigate innovation outcomes associated with complementary sets of training practices. Our analysis is performed using a multiple linear regression model with lagged variables on several different service sectors. We lagged three training and technology factors and noted the extent of innovation within and between these factors while comparing returns to innovation in the short-term (one year) to the long-term (the following six years). We hypothesised that the complexity of technology and process of learning by doing/using would result in short-term innovation returns being far less than those experienced in the long-term. We predicted the opposite would occur for the training factors due to the obsolescence of acquired skills over time. Our results show that short-term innovation returns for training factors are consistently higher than those for technology. This lends support to our hypothesis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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