Bibliographic record
Abstract
This paper proposes a system approach to regional economic resilience. This approach argues that regional economies undergo, to varying degrees, changes to their economic system that result from the collective but uncoordinated action of economic actors in an attempt to be resilient to shocks. The system change, particularly focusing on changes to economies’ structure and function, which occurs during and following a shock, determines the type of resilience (i.e., engineering, ecological, evolutionary and transformative) employed by regions. The type of resilience employed can influence regions’ long-term growth trajectory and resilience to future shocks. This approach advances the examination of regions’ resilience capacity, which has largely been ignored in empirical studies of resilience. In doing so, the approach developed in this paper is heuristic rather than deterministic, with the latter characterizing the bulk of the literature. A greater investigation into system change can provide a holistic understanding of resilience. This approach has many advantages, such as developing greater insight into resilience, applying a heuristic method rather than deterministic and examining regions’ adaptive capacity. To advance the system approach, this paper provides greater conceptual clarity of resilience, highlighting the notions conceptual parameters and rethinking the oppositional context in which the four main types of resilience are commonly discussed. Specifically, it conceptualizes the main types of resilience as complementary rather than oppositional. The overall contribution of this paper is twofold. First, it establishes a greater conceptual framework of resilience. Second, it develops an approach in which regions’ adaptive capacity can be investigated.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".