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
This study theorizes about the behavioral and knowledge creation implications of betting on the losing technology in a competing technology situation and focuses on three main outcomes. First, in a situation with competing technological options, firms that invest initially in the losing technology will be less successful subsequently in building new knowledge in the winning technology because their experience with failure will lead them to update their expectations of the industry and choose to pursue less risky alternatives. Second, two classic risk-reducing strategies—investing in both technologies or entering after uncertainty is resolved—will not be completely effective. Firms investing in both technologies are likely to suffer the incentive and coordination-driven innovation penalties of generalists, while late entrants will suffer learning disadvantages. Third, the possession of key and relevant complementary assets—upstream and downstream—will positively moderate the observed inertial effect on firms that backed the failed technology and generalists that backed both technologies, as these complementary assets will increase incentives to adapt to the winning technology. I find empirical support for my hypotheses using a novel data set on the evolution of the global flat panel display industry from 1964 to 2003 to investigate the technological competition between plasma and liquid crystal displays. Results show that firms initially pursuing plasma generated less subsequent knowledge in liquid crystal displays, and that firms betting on both technologies were also slow to build knowledge in liquid crystal displays. Meanwhile, firms with upstream and downstream complementary assets were able to moderate, but not overcome, this barrier to knowledge creation. The findings have implications for our study of technological evolution and adaption, for learning from failure and reinforcement learning, and for the relationship between resource partitioning and adaptation.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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