Where Now for Post-Normal Science?: A Critical Review of its Development, Definitions, and Uses
Bibliographic record
Abstract
‘‘Post-normal science’’ (PNS) has received much attention in recent years, but like many iconic concepts, it has attracted differing conceptualizations, applications, and implications, ranging from being a ‘‘cure-all’’ for democratic deficit to the key to achieving more sustainable futures. This editorial article introduces a Special Issue that takes stock of research on PNS and critically explores how such research may develop. Through reviewing the history and evolution of PNS, the authors seek to clarify the extant definitions, conceptualizations, and uses of PNS. The authors identify five broad areas of research on, or using, PNS which have developed over four decades. Their analysis suggests that the 1990s represent a symbolic watershed in the use of PNS terminology, when the concept was further developed and applied to highly complicated issues such as climate change. The authors particularly distinguish between uses of PNS as a normative prescription and as a practical method. Through this classification, they set out gaps and research questions arising. They then briefly summarize the Special Issue articles and consider their relationship to each other and the research questions raised by their analysis. They conclude by considering what the articles in this issue suggest for future theory building in PNS and related scholarship.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".