Firm and non-firm actor collaborations as a determinant of countries' readiness, progress and success for developing COVID-19 vaccines
Bibliographic record
Abstract
Using the national technological capability (NTC) approach, we examine the influence of different configurations of firm and non-firm actors' collaborations on countries' level of readiness, progress and success for developing a COVID-19 vaccine. We create a country index which captures the spectrum from readiness, progress to success. The effects of NTC macro-level determinants and the micro-level collaborations on the index are informative. Higher levels of progress and success by countries are determined by: 1) NTCs which focus on sound supporting healthcare institutions; 2) advanced NTCs and advanced biopharmaceutical sector capabilities which also lead to better global collaborations by firm and non-firm actors; 3) non-firm sector collaborations. For lower readiness and progress countries: 1) the bulk of knowledge for developing a vaccine resides in interfirm collaborations; 2) non-firm collaborations negatively impact their readiness, progress, and success. We discuss the implications of these results for policy, practice, and future research.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".