Bibliographic record
Abstract
Abstract By 1984 the operation of Canada’s major airports was costing the federal government over $750 million a year which was increasingly not politically acceptable. Starting in 1985 the response was to create local nonprofit Canadian airport authorities. By 2008, 21 airport authorities had achieved far more success than initially expected. Since 1992 they have spent over $32 billion in infrastructure development. They employ almost 100,000 people directly and indirectly in airport operations, spend almost $7 billion a year in local economies, and contribute $400 million a year to the General Revenue Fund. This remarkable success has been in place for thirty years. The governance framework and accompanying management and oversight rules under which the airport authorities operate have not fundamentally changed since 1992, even though designing and implementing the reform proceeded across Liberal and Conservative governments. Creating public policy is usually long, tedious, and fraught with pitfalls and traps. In this case policy development was undertaken in an unusual manner, having to contend with impatient political leaders and extremely conservative and suspicious public bureaucrats who saw this as a threat to their domains. This chapter tells the story of the creation of Canada’s airport authorities and the innovative administration and policy development that allowed that to occur.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.206 | 0.055 |
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".