Bibliographic record
Abstract
[Extract] Automobile clubs have a history of service to automobile owners that commenced shortly after the first cars began appearing on the road. In Figure 1.2 (p. 10), attention was drawn to the importance of automobile clubs that, along with other destination pull factors including policing, the highway network, attractions, accommodation, and so on, create the drive experience. Today, automobile clubs continue to provide a comprehensive range of services, including many that make a direct contribution to the enhancement of the drive tourism experience. As this chapter demonstrates, the role of automobile clubs has expanded over past decades while membership rates remain high in many nations. In the US, for example, there are over 50 million members of the 69 affiliated clubs in the American Automobile Association (AAA), while in Queensland, Australia, the Royal Automobile Club of Queensland (RACQ) has a membership of about 1.2 million, or a quarter of the state's population. This chapter briefly reviews the roles and functions of automobile clubs, with a specific emphasis on their contribution to drive tourism. The chapter concludes with a case study that examines the contribution made to drive tourism in Queensland, Australia by the RACQ.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".