Bibliographic record
Abstract
Motor vehicles as they appear in the world today are mostly made by the major automakers, which includes a blend of commercial vehicles. However, between the big automakers and the consumer, there exists another automotive industry, or sub industry. Collectively it is a large industry, consisting of numerous and various smaller companies. These companies manufacture the tens of thousands of specialized vehicles that are operating on the roads today. This is the industry that takes mass produced motor vehicles from the major companies and makes and shapes them into the many types of special vehicles demanded by the market place - custom vehicles that the major companies cannot mass produce - such as school buses, emergency vehicles, recreational vehicles, wheelchair accessible vehicles and an endless array of heavy trucks. The Enforcement section in the Transport Canada Road Safety Directorate has been strategically monitoring and regulating this industry for forty years under the mandate of the Motor Vehicle Safety Act. This paper is about the Motor Vehicle Safety Act and attendant regulations; how they affect Canadian companies that manufacture or import specialty motor vehicles - including companies that modify vehicles for wheelchair access. The paper intends to show that overall, existing regulations, perceived by some to be written for the major companies, do not adversely affect smaller less sophisticated companies like adaptive vehicle modifiers. Complex regulations can actually serve to motivate smaller specialty companies to raise their level of expertise, to a level that is more in line with the testing and technology contained in the original factory vehicles they are modifying. The paper explains that the regulations are minimum standards, based on industry manufacturing practices, which should be enforced and not relaxed. The paper attempts to demonstrate how enforcement of regulations can promote the larger manufacturers of vans and chassis to communicate with the smaller up-fitters to produce a more competitive product in the end. It advocates that any kind of relaxing of regulations or enforcement towards smaller manufacturers will not benefit disabled persons or other end users and will only make room for irresponsible companies.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".