Information overload and the ossification of immunological research
Bibliographic record
Abstract
Information overload and the ossification of immunological research Looking at how universities fund science and appoint faculty, as well as evaluating papers for publication – there comes an issue with the exploration and dissemination of the most innovative ideas and findings, argues Peter Bretscher, Faculty in the Department of Biochemistry, Microbiology and Immunology at the University of Saskatchewan. Addressing two foundational questions as to how immune responses are regulated and how answers may guide the prevention and treatment of clinical conditions associated with infectious diseases, autoimmunity, and cancer, Bretscher explores potential medical use in model systems for the prevention and treatment of disease, which still sit outside of mainstream immunology. In order to foster resilience in immunological research, he employs contemporary immunology as a case study, and proposes two parallel panels: a conventional one, and an alternative panel, also consisting of eminent researchers, but in neighbouring fields. The alternative panel would then fund more truly innovative proposals that challenge dominant frameworks, resulting in more impactful research.
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 | 0.146 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.012 |
| Science and technology studies | 0.010 | 0.078 |
| Scholarly communication | 0.040 | 0.054 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.010 | 0.007 |
| 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".