British Columbia’s fast ferries and Sydney’s Airport Link: partisan barriers to learning from policy failure
Bibliographic record
Abstract
This chapter examines situations in which the incentives of partisanship can encourage a government to actively seek to exacerbate an existing policy failure rather than to repair it. Under these circumstances, the certain benefits of shaming the political opposition outweigh any potential rewards of improving specific policy outcomes. The chapter considers two cases of policy failure in the late 1990s in the transportation sector. The first case explores an effort by the British Columbia Ferry Corporation (BC Ferries), a public provider of marine transportation on Canada's west coast, to introduce a fleet of high-speed aluminium catamaran ferries (the ‘fast ferries’). The second case investigates a public–private partnership scheme to build and operate an urban rail link between the central business district and the airport in Sydney, Australia (the Sydney Airport Link). In both cases, policy options were presented that had the potential to mitigate financial losses and to redirect the project back toward the achievement of stated policy objectives. However, these options were rejected by decision-makers in favour of actions that did nothing for the success of the project but that did deliver some short-term political and electoral rewards.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 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".