Racing with Approval: Developing a Methodology to Measure Social License for Formula 1 Grands Prix in Montreal and Las Vegas
Bibliographic record
Abstract
This report develops a methodology to estimate the degree of social license garnered by Formula 1 Grands Prix, focusing on the Canadian Grand Prix in Montreal and the Las Vegas Grand Prix. Drawing on the Social License to Operate (SLO) Pyramid (Thomson and Boutilier, 2011) and Arnstein’s (1969) Ladder of Citizen Participation, a framework was created to assess the levels of public participation and acceptance. The report identifies key indicators of social license and public engagement using a mixed-methods approach through a literature review, document analysis, media scan, and semi-structured interviews. The findings reveal that while Formula 1 Grands Prix are widely embraced for their international prestige and perceived economic benefits, the public engagement processes are frequently characterized by tokenistic practices, raising critical concerns about transparency, inclusivity, and the authenticity of participation mechanisms. Based on these findings, the report proposes recommendations to strengthen social license, including implementing digital communication tools, expanding notification procedures, developing public education initiatives, early-stage community engagement prior to event bidding, and enhanced transparency regarding economic impacts. These measures foster meaningful citizen participation and promote a more sustainable and socially accepted model for hosting Formula 1 Grands Prix.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".