Thoughts on Competitiveness and Integrated Industrial Policy: A Field of Mutual Convergences
Bibliographic record
Abstract
Competitiveness and industrial policy seem to play a critical role in the development and mutation of different spatialized socio-economic systems. This article aims to review the literature on these two concepts and suggest a novel theoretical framework. First, we identify that, in the relevant literature, industrial policy acquires progressively a repositioned content, described as a new, holistic, multidimensional, or integrated policy that can help create and sustain the competitiveness of the firms, industries, localities, nations, or other socio-economic agglomerations. In this context, we explore the form of an actual integrated industrial policy and propose the theoretical framework of the competitiveness web, in which the co-evolution of micro-meso-macro levels are explored, by placing the dynamics of business innovation at the dialectic center of the overall developmental process. This integrated industrial policy to strengthen competitiveness must also be able to promote innovation in the different local and regional ecosystems and, therefore, we conceive a policy mechanism in the form of the Institutes of Local Development and Innovation (ILDI). The primary purpose of these institutes is to diagnose and strengthen the Stra.Tech.Man physiology (strategy-technology-management synthesis) of the local socio-economic organizations. We believe that this new approach to the integrated industrial policy to strengthen the local competitiveness can contribute to facilitating the adaptation of the socio-economic systems, and especially the less dynamic and developed, to the new emerging challenges of the crisis and restructuring of globalization in the pandemic 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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".