Bibliographic record
Abstract
Internationally, governments have devolved responsibility for transport support industries from the public sector to the private sector. In Canada, this happened to airports under the Mulroney Conservative government through the creation of Local Airport Authorities (LAAs), and latterly through the implementation of the 1994 National Airports Policy (NAP) of the Chrétien Liberal government. Sufficient time has passed to consider the progress of this policy experiment. Over 1999-2000, the Federal Government reviewed its airports policy (with respect to the first set of LAA transfers) and the Auditor General devoted one chapter in his annual report to airport transfers under the NAP. The airport devolution process in Canada has been a success in many respects, but airport systems are complicated, capital intensive and long-lived. The success of airport commercialization in the longer term is yet to be determined. Many policy makers in other countries have been interested in the Canadian model of airport devolution. When Transport Canada began to contemplate devolution of the airports in the mid-1980s, its vision was not strictly privatization. Devolution policy embraced a combination of public ownership and private sector management of operations, termed as “commercialization.”
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.026 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.078 | 0.008 |
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".