Technological exaptation and crisis management: Evidence from COVID‐19 outbreaks
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
One of the key issues in the field of technology analysis and innovation management is how new technologies origin and evolve in the presence of environmental threats. We confront this problem focusing on emerging innovative solutions to cope with unexpected and harmful problems posed by crises and needing a rapid, effective response. We specifically analyze the patterns of critical innovations to cope with new coronavirus disease (COVID‐19) that is generating public health and economic issues worldwide. Accordingly, in the context of the theory of technological exaptation, we adopted a narrative approach examining vital innovations that ended up treating COVID‐19 even though they were originated to treat other diseases (more or less distant from the COVID‐19 domain), as the antiviral drug Remdesivir and the antirheumatoid arthritis drug Tocilizumab. Results reveal that technological exaptation, especially if characterized by a longer exaptive distance, is a potential driving force of innovation to cope with COVID‐19 in the short‐term and other similar issues. On this basis, we provide propositions for a more general crisis model of innovation. This study adds a new perspective that may be helpful to explain the evolution of innovation in the presence of crises, considering technological exaptation in a context of environmental threats.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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