Bibliographic record
Abstract
Structural change in container port operation and ownership over the past decade has seen the emergence of port-operating transnational corporations (TNCs). The emergence of the port-operating TNC requires a fundamental epistemological shift in reconceptualising the port, from a single, fixed, spatial entity to a network of terminals operating under a corporate logic. This shift is twofold. First, because under port reforms corporate entry occurs overwhelmingly at the terminal level, the terminal rather than the port becomes the relevant spatial unit of analysis. Second, although spatial theories of the firm represent a longstanding stream in economic geography, such theories have yet to find general application in port studies. Consequently, in addressing the interface between transport and economic geographies, the authors suggest a geography of the port-operating TNC as a potential bridge. A decade of privatization in the port sector has rendered the industry an appropriate empirical ground for enquiry into spatial theories of the firm. Evidence from Asian port systems and business networks are put forward in sketching a new research agenda.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.017 | 0.038 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".