Bibliographic record
Abstract
Many aspects of vaccine development, efficacy and safety deserve the utmost of attention as unparalleled progress in this regard occurs at breakneck speed for COVID-19. Aspects of vaccine safety that proportionately lack discussion even publicly in the scientific arena, whether inadvertently or purposely, are the issues of mutagenesis or oncogenesis. As Ball suggests, anti-vaccine postures will arise and potentially gain momentum in the context of a general hesitation for vaccination among some in the populace (1). Any damage done in the SARS-CoV-2 vaccine movement will undoubtedly be seen by some as collateral damage to the general concept of vaccination, especially if it is understood that corners will be cut for expediency. It is imperative, therefore, to ensure that a more comprehensive view of vaccine safety is well discussed, if not acted on, at least initially in scientific fora. As generally known, there are many approaches currently being investigated for SARS-CoV-2 vaccine development. Some of these, although not necessarily new, will be brought to clinical application and may include such new products as mRNA vaccines, plasmid or similar DNA constructs, and different virus-based carrier systems in the least. Safety will undoubtedly be analysed for short- or intermediate-term adverse effects. Will any such study, whether Phases 1, 2, or 3, be of sufficient number and timing to understand any opportunity for cellular transformation?
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.
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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".