Public sector innovation and the constraints of ‘platform thinking’: An account of Johnson & Johnson's adenoviral vector vaccines
Bibliographic record
Abstract
CONTEXT: Scholarship on political economy of vaccines in the COVID-19 era has focused on mRNA. Yet Johnson & Johnson's (J&J) vaccine based on recombinant adenovirus type 26 (Ad26) was effective against COVID-19, widely distributed, and earned billions in revenue. The story of J&J's "proprietary" Ad26-based, "AdVac"-branded vaccine "platform" spans decades and multiple pathogens besides SARS-CoV-2, including HIV and ebolavirus. The AdVac "platform" exemplifies the role of the "platform" in modern vaccine development. Our work asks: what is a vaccine "platform"? What role do platforms play in "assetization" of science? METHODS: We conducted a qualitative study of the history of AdVac, triangulating patents, scientific literature, other documentation, and interviews with key scientists. We constructed a timeline of the three phases of the "platform's" life: early promise, mixed success driven by public investment, then disappointment and divestment. FINDINGS: We distinguish "platforms" from vectors by incorporating analysis of the social, political, and economic context in which vectors operate. "Platform thinking" by scientists in industry, academia, and government can drive claims that certain vectors have all-purpose utility while overlooking other components as mere details. When the Ad26 vector's totalizing potential as a "platform" lost credibility, J&J divested from vaccine research, leaving important scientific questions unanswered and technical resources unshared. CONCLUSIONS: Scientists must recognize platform thinking to prevent it from unduly shaping the trajectory of biomedical research. Political and scientific leaders should invest in public-sector capacity so that promising technologies can be brought to the public without need for an industry partner in every instance.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.048 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".