British Columbia’s Fast Ferries and Sydney’s Airport Link: Partisan Barriers to Learning from Policy Failure
Bibliographic record
Abstract
Introduction Policy learning, where experiences from other jurisdictions and time periods inform decision-making, has been suggested as a way to improve policy outcomes – or at the very least, to improve a government's ability to predict the outcomes of its own policy decisions (Mossberger and Wolman, 2003, 430). Policy failures might therefore seem to have an especially prominent place in the learning process, as examples of instruments and ideas to avoid. Nonetheless, episodes in which failure did not lead to lessons learned or to improved public policy are abundant. The implication of this non-learning from failure is that there are situations in which the consequences of failure may not be a strong enough deterrent to prevent failure from re-occurring. In this chapter, we explore one such situation, 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. Using the cases of British Columbia's fast ferries and the Sydney Airport Rail Link, we develop a scenario in which policy failure leads not to policy learning but rather to deliberately increased failure. While democratic governments have long been thought to endeavour to improve social outcomes, at least for particular groups or individuals (Downs, 1962), in some cases incentives can exist for governments to do more harm than good. To this end, we will examine 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’), and the second 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).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".