The real cost of the automobile: a report on recent research
Bibliographic record
Abstract
This article provides a comprehensive survey of the views of a number of experts on the real costs of the au-tomobile. The costs of the automobile are extensively categorized, and the most significant of these catego-ries are analyzed and quantified. The article also in-cludes a case study on the costs of the auto industry in Ontario, as well as an extensive bibliography. The automobile carries us to our birth, conveys us to the grave, transports us on the errands of mortality, and stands parked at the center of our energy problem: The freedom of mobility it grants costs us about 30 percent of all the petro-leum we burn. It has been, and still is, a costly status symbol. —D. Jeffery (1981, p. 24) There have been many attempts to identify and quantify the true costs of the automobile. The purpose of this article is to present and consolidate the most recent findings of researchers and commentators in this area. It provides a comprehensive list of categories for the true costing of the automobile and proceeds to present the views of numerous experts on some of the most significant of these categories. It concludes with a summary of a recent case study on the costs of the automobile in the province of Ontario, Canada. An extensive bibliography has been appended. Categories for the True Costing of Automobiles In the early 1990s, Mark Delucchi made an exhaus-tive study of the real costs of motor vehicles. The cate-gories outlined in this article are largely taken from his
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.031 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".