Analyzing some concepts of immune regulation of the last three decades: Fostering greater research resilience despite the information overload. A personal view
Bibliographic record
Abstract
There is considerable interest in whether increased investment in science, made by society, pays dividends. Some plausibly argue the increased rate of production of information results in an ossification of the canon. Reports, challenging the canon, fall by the wayside. The field thus becomes increasingly complex, reflecting not so much the reality of nature but how we investigate the subject. I suggest that focusing on and resolving the paradoxes evident within a canon will free the logjam, resulting in more resilient research. Immunology is among the fastest growing of biological sciences and is, I suggest, an appropriate case study. I examine the commonly accepted frameworks employed over the last three decades to address two major, related immunological questions: what determines whether antigen activates or inactivates CD4 T cells, and so whether immune responses are initiated or this potential ablated; secondly, what determines the Th subset to which the activated Th cells belong, thus determining the class of immunity generated. I show there are major paradoxes within these frameworks, neglected for decades. I propose how research focused on resolving paradoxes can be better fostered, and so support the evolution of the canon. This perspective is pertinent in facing critical issues on how immune responses are regulated, and to more general issues of both the philosophy of science and of science policy.The last section is in response to questions and comments of the reviewers. It brings together several considerations to express my view: the same frameworks, formulated in response to the two questions, are useful in understanding the regulation of the immune response against model antigens, against self and foreign antigens, those of tumors and of pathogens.
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 | 0.041 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".