Extending the theory of planned behavior: Predicting the use of public transportation
Bibliographic record
Abstract
An expanded version of the theory of planned hehavior (TPB) was used to predict and explain public transportation use. A pre-post design was used to examine changes in university students ' bus ridership after the implementation of a universal bus pass (U-pass) program. Bus ridership significantly incrcascd aftcr the U-pass was implemented and associated changes in attitudes and beliefs about transportation modes were found. In both phases, students ' public transportation use was well predicted by the original TPB. However, 2 additional constructs-a descriptivc norm. and the interaction between intcntion and perceived behavioral control~~~significantly improved prediction in both phases of the study. These constructs might be useful additions to the original TPB, at least in this behavioral domain. Among the many environmental problems wc now face, global climate change (global warming) is one of the most serious. Human-caused greenhouse gas emissions are important contributors to global climate change (Oskamp, 2000). In British Columbia (B.C.), Canada, transportation is the largest source of greenhouse gases (B.C. Ministry of Energy, Mines, and Petroleum Resources;
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".