Technological exaptation and crisis management: Evidence from COVID‐19 outbreaks
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".