Modelling Journey to Work Patterns in South East Queensland, Australia
Bibliographic record
Abstract
Journey to Work (JTW) describes the transportation mode used by an individual to travel from home to work. The paper analyses and models the spatial patterns of JTW. Using South East Queensland – a large conurbation and a popular destination for seachange, this study examined the aggregate JTW census data to capture travel to work patterns and the contextual factors that underpin different transportation modes. The results show heavy reliance on private cars to commute to work. Employment concentration, accessibility to the CBD and the number of bus stops per square kilometre all have a positive impact on public transport users; while the proportion of industrial land use to total zoned land elicits a negative impact on public transport users. These data suggest that the commute to industrial zones, which are largely located in suburban areas, necessitates the use of private cars; conversely better accessibility to the Central Business District via public transport encourages commuters to use public transport. However, the data uses in this study do not account for people who work from home, or whether the commuter’s employment is temporary or permanent, or account for the extent to which people’s work involves visiting multiple locations in one day.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".