Federal government failure in Canada
Bibliographic record
Abstract
The discussion of the limitations of government and subsequent government failure is wholly absent from debate in Canada where, unfortunately, we still assume that governments act benevolently and without institutional constraints. That this is not true is plain to see in the pages of the Auditor General of Canada’s reports, which provide concrete evidence of the existence and extent of federal government failure in Canada.The reports issued by the Office of the Auditor General of Canada (hereafter, Auditor General) are an excellent source of tangible examples of federal government failure that illustrate Public Choice theory. A total of 614 instances of government failure are included in this volume. All cases were derived from reports of the Auditor General published between 1988 and 2013.For the purposes of this study, government failure is defined as a failure to achieve the stated goal(s) of a program or initiative. It is a more narrow definition than what is commonly used, which includes evaluating the efficacy of a program or initiative. This study undertakes no such evaluation but simply uses the Auditor General reports to determine whether there were problems in program design, delivery, and effectiveness.
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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".