The new normal: governance, disruption and the post-truth era
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the new normal within a continuum of three types of disruption, each of varying duration. References to the new normal draw attention to the periodic and rising importance of different levels, types, and consequences of game-changing disruption for those in governance roles. Design/methodology/approach In this conceptual research, given the discussion of a return to normalcy near the expected end of the COVID-19 pandemic, the authors organize the literature on disruption in governance into a disruption continuum – emergency, crisis and super crisis – to demonstrate the differences in each type of disruption to establish a distinct view of the new normal. Findings Within the three types of disruption, the first two suit the rational authority model in which disruption is turned over to those in governance roles. However, the rational authority model comes under attack in the super crisis and is increasingly associated with the post-truth era. Social implications In Type 3 disruptions or super crises, the failure of those in control to set the parameters of the new normal raises concerns that the center no longer holds, and as a result, the assumption of an attentive public splinter into multiple contending publics, each with its version of data, facts and images. Originality/value The new normal is typically treated after the result of a black swan or rare and surprising long-lived disruption. In this work, the formulation of the recurrence, ubiquity and controversy engendered by super crises suggests that it is one of the features attenuating and giving rise to fractious incivility in the post-truth era.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".