Firm and non-firm actor collaborations as a determinant of countries' readiness, progress and success for developing COVID-19 vaccines
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
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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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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