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
Researchers have cited significant gaps in our knowledge regarding the early stages of vision formation in the radical innovation context and have emphasised the importance of further investigation in this area. As such, this paper aims first to build on the extant literature on organizational, project and M arket V ision in order to construct a measure for T echnology V ision through theory construction, scale development and modeling. The second goal is to help firms to better understand what the underlying components of Technology Vision are in order to offer themselves the best possible chance of success with the development of radically new, high‐tech products. Based on samples of firms involved with radical innovation research and development in high‐tech sectors in N orth A merica and the U nited K ingdom, conceptual and measurement studies conducted herewith suggest there are five factors related to T echnology V ision: T echnology V ision benefits, T echnology V ision efficiency, T echnology V ision magnetism, T echnology V ision specificity, and infrastructure clarity. The paper concludes with an examination of the implications of these components of T echnology V ision and discusses the need to understand its relationship with M arket V ision and the performance of the firm.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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