Bibliographic record
Abstract
During the second half of 2008, worldwide stock exchanges plunged, the investment bank Lehman Brothers declared bankruptcy and commodity prices collapsed. These are all signs of how the late 2000s financial crisis hit the world economy. With this hit of the world economy, global trade in goods was obviously also affected. Despite the fact that there was a seemingly unstoppable growth in global trade for many years, the late 2000s financial crisis slowed down this growth and turned it into a decline. Simply stated, a lack of consumers’ trust in the economy lead to the postponement of expenditures, resulting in a lack of businesses’ trust in the economy and the layoff of employees: a negative vicious circle. The Port of Rotterdam, Europe’s largest port in throughput of containers, cargo tonnage and transshipment, was (not surprisingly) also impacted by the crisis. From the third quarter of 2008 until the second quarter of 2009 the port reported a downfall in throughput for four quarters in a row. A clear sign of the fact that things did not went well for the port. The question is: how bad was it? Or in a more scientific form: what has been the impact of the late 2000s financial crisis on the performance of the Port of Rotterdam? This thesis tries to show the reader the impact of the crisis in the Port of Rotterdam by following a step-by-step procedure. The main idea behind this is the fact that the explanation of how the crisis impacts the port is rather complex. Within Rotterdam’s port area many different types of companies and organizations are operating and almost all of them responded in a different manner to the crisis. In order to cope with this, it is first clearly determined what we want to measure in order to be able to answer the question stated above: port performance indicators are created. Based on these measures, expectations for the port are outlined and finally, an analysis of the port’s performance will take place. In the analysis, the impact of the crisis on the added value generated by companies within the port, the number of employees working at companies within the port and the number of business establishments within the port is explained extensively. It shows the effect of the crisis and compares the effects on different industries by using input output models. In doing so it is clarified why certain sectors performed fairly well despite the fact that the crisis was present (energy companies), whilst others, such as the petroleum- and chemical industry faced heavy downfalls in added value. Where some companies had to lay off a large share of their staff due to a fall in added value, others were able to retain their employees. Furthermore, the indirect effects of the crisis in the Port of Rotterdam are discussed. These are the economic effects that are caused in other parts of the country due to the presence of the Port of Rotterdam.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".