Guiding Principles for Developing Digital and Projected Advertising Display Regulations
Bibliographic record
Abstract
Digital and projected advertising displays (DPADs) are a rapidly emerging form of roadside advertising. The brightness of DPADs and their ability to distract drivers by displaying dynamic content are causing concern about their road safety impacts. Most jurisdictions have sign by-laws designed to control the time, place, and manner of static advertising signs. However, many jurisdictions have not updated their by-laws to address the unique characteristics of DPADs. Advertising is a form of expression which is considered a fundamental freedom under the Canadian Charter of Rights and Freedoms. Advertising is also a type of roadside information that is not essential for the driving task and by its nature increases the risk of collision by distracting drivers. Jurisdictions must balance the right to advertise with the potential road safety risks of these advertisements. Therefore, the objective of DPAD regulations is to control the negative road safety effects of DPADs. This paper presents five principles to guide professionals during the development of DPAD regulations from a road safety perspective as part of a larger study to develop national guidelines for the application and use of digital advertising signs. These principles are safety, consistency, specificity, evidence-based, and pragmatism. These principles help ensure that regulations hold public safety as the primary concern, result in DPADs operating in a manner that emulates static advertising signs, pertain only to issues unique to DPADs and not to issues that are common to all advertising signs, are evidence-based, scientifically supported, and sensitive to local conditions, and are pragmatic and enforceable.
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 | 0.147 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.024 | 0.010 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 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".