Economic complexity and the dynamics of regional competitiveness a systematic review
Bibliographic record
Abstract
Purpose This study aims to systematically review the economic complexity literature to advance the knowledge on its contribution to building regional competitiveness. Design/methodology/approach In this study, we did a systematic review of 111 relevant papers. In this regard, we did a thematic analysis on all the collected papers, which led to a two-level processed approach. In the first level, the contributions of the reviewed articles have been classified into three main streams. In the second level, the findings under each contribution category are analyzed and explained. This approach led to a thematic network demonstrating economic complexity and the dynamics of regional competitiveness and a set of managerial and policy implications. We followed a multiple processed approach for the systematic review of 95 papers that reveals considerable contributions in three categories, including measurement techniques, criticisms and exploratory studies. Findings Despite some critiques and the undertaken evolution in measurement techniques of complexity, economic complexity has become a well-known method mainly for regions' competitiveness dynamics. Our review demonstrates a nested network of economic complexity dynamics that drives policy advice concerning countries' status in their development path. The provided set of policies includes guidelines for underdeveloped and developing countries and general policy implications, applicable for all regional contexts for building competitiveness dynamics. Originality/value This research contributes to the literature on competitiveness from the window of economic complexity. The study allows a deep understanding of regions' productive structure role in their development and competitiveness. A set of policies for building regional competitiveness is provided concerning the study's findings. The literature gaps are identified, and future research ideas are provided for using economic complexity methodologically and logically to boost regional competitiveness.
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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 | 0.030 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| 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".