Bibliographic record
Abstract
Case studies examined how airport operators addressed bicycle access to their properties and the motivations and obstacles that they faced in light of new policies to integrate bicycles, along with transit and walking, into transportation planning, design, and construction and to increase the bicycle's role in the transportation system. Eight influential elements that emerged from the review of policy documents and research literature were used to guide interviews: governance structure, location, access roads, self-perceived environmental stewardship, spending restrictions on nonaviation transportation improvements, proximity to transit, policies and mandates to reduce environmental impacts, and land use constraints. Seven cases were selected on the basis of their inclusion in studies on key aspects of airport ground access: Oakland International Airport, San Francisco International Airport, and Los Angeles International Airport, California; Seattle–Tacoma International Airport, Washington; Boston Logan International Airport, Massachusetts; Minneapolis–Saint Paul International Airport, Minnesota; and Portland International Airport, Oregon, an exemplar recommended by several informants. The discussion was limited to employee bicycle access, the focus of airport operators that invested in programs to reduce single-occupancy-vehicle travel at airports. During aggregation of the interviews, replicable approaches for improving bicycle access were identified, as were examples of innovative funding for multimodal access that used revenues generated through passenger facilities charges. The following areas are suggested for additional research: commute needs of airport employees, mode choice for ground access, and airport operator costs and benefits of bicycle access investments.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".