Post‐Reunification Restructuring and Corporate <scp>R</scp>e‐bundling in the <scp>B</scp>itterfeld‐<scp>W</scp>olfen Chemical Industry, <scp>E</scp>ast <scp>G</scp>ermany
Bibliographic record
Abstract
Abstract While earlier research has shown that regional restructuring after reunification has led to broad de‐industrialization processes in eastern Germany's chemical industry, this article focuses on how re‐bundling processes at the corporate level have stimulated adjustments to the changing economic and political environment leading to a renewed regional development trajectory. The analysis is based on a conceptualization that assesses diachronic processes of rupture and re‐bundling by applying a bottom‐up perspective of how corporate adjustments and restructuring processes generate re‐bundling types that manifest themselves in broader regional re‐bundling scenarios. The empirical analysis focuses on a qualitative case study of Bitterfeld‐Wolfen, the eastern region with the largest chemical industry. The research provides evidence that, although new firm formation has remained weak and acquisitions of chemical multinationals have generated structures only tenuously embedded in the regional economy, modernization and re‐bundling process have contributed to a renewed, smaller yet stable, regional chemical industry. The analysis further shows that the associated processes depended on the roles of individual industrial leaders in the region, who acted as network builders, mobilized joint action and stimulated the development of a collective regional spirit.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".