Which businesses are for and against “pop-up” cycleways: the case of Brisbane's CityLink Cycleway
Bibliographic record
Abstract
COVID-19 accelerated “pop-up” cycleway initiatives in cities across the world, which are often contentious for the business community in affected streets and neighbourhoods. Brisbane converted parking lanes to form the CityLink Cycleway in its central business district. While the project was positively received overall, there was vocal business opposition, primarily on the grounds of loss of parking and loading bays. A set of field surveys collected 303 valid responses: 44 from businesses, 247 from customers, and 10 from delivery workers. The results show businesses erroneously perceive customers’ travel modes, overestimating car usage, and underestimating the mode and expenditure share of customers who walk, use public transport, or ride bicycles or powered mobility devices. Customers and delivery workers had more positive responses about the cycleway, while businesses expressed mixed reactions. Retailers had a less accurate picture of their customers' modes of travel, than those running restaurants and cafés, as did business owners/managers who drove to work. Respondents’ suggestions for city centre access improvements were mixed, especially regarding crowding. The overall positive responses captured by the study demonstrate there is a strong case to retain the CityLink Cycleway longer-term. However, there are concerns from businesses and other users regarding loading bays.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.012 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".