From local action into national discourse: the rise of Greenport Venlo as a Dutch international logistics hub
Bibliographic record
Abstract
The rise of global supply chain systems and the dispersion of related logistics centers further inland has led to a new phase in the evolution of port systems, referred to as port regionalization (Notteboom and Rodrigue, 2005). While this process is largely the result of the decisions of shippers and logistics providers, there is scope for public policy to shape its spatial contours. This includes the strategic capacity of stakeholders to couple not only locally available assets with the needs of global flows, but also to provide meaning of a place through the constitution of symbols, frames and discourses. As such, we propose to apply a relational perspective to port regionalization, which allows us to analyze how various actors interact and form coalitions to secure their interests in a multi-scalar governance context. We will do so by presenting the rise of Venlo in the Netherlands as an international logistics hub within the corridor of the Port of Rotterdam, the seeds of which were laid in the early 1990s. This case study highlights how a local-based coalition consisting of shippers, logistics providers, local governments and the agri-food sector managed not only to insert Venlo in Rotterdam-based corporate networks and transport flows, but also to include Venlo in the national Mainport-policy discourse by invoking the frame of the 'Greenport'. The local coalition of Venlo territorialized this ‘Greenport’ policy-frame to legitimize further economic development based upon logistics.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".