Bibliographic record
Abstract
Purpose The purpose of the paper is to set out an evolutionary schema for organizational resilience using the established emergent autopoiesis coherence (EAC) framework, with empirical reference to the European Parliament’s development of institutional capacities since its foundation in 1952 as the Common Assembly of the European Coal and Steel Community (CA-ECSC). Design/methodology/approach The logic is categorical-synthetic and second-order cybernetic, implicitly underlain by a correspondence theory of truth united with a coherentism based in the epistemology of complex systems. Findings The European Parliament has constructed itself as a resilient organization, but the process has entailed over-learning of past lessons, creating behavioral syndromes of dysfunction in the face of new challenges. Research limitations/implications The work contrasts antifragility with resilience and suggests a new approach to it. Practical implications The analytical framework and conclusions hold value for the practical design of resilient organizations. Social implications The groundwork of the EAC’s conceptual framework is laid, and the basis for applying it to human and other naturally occurring societies is established. Originality/value K.W. Deutsch’s mid-twentieth century work on cybernetic-based learning in political systems is reconstructed, updated and applied to twenty-first century political phenomena. The insights are validated, and the analytical framework’s robustness is demonstrated.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".