A Brief Intermodal Rail Network (IRN) Scale: Establishing Validity and Reliability
Bibliographic record
Abstract
An Intermodal Rail Network (IRN) Scale was created for the purpose of measuring consumer perceptions about their experiences with airport intermodal rail travel. Some previous research has focused on intermodal rail in other arenas, but to date, no paper that we know of has developed a valid and reliable scale to measure consumer perceptions of airport intermodal rail travel. The indirect purpose of this scale is to aid in measuring the factors that would influence the use of an IRN, wherein the greater the usage, the greater the efficiency, and therefore reduce the number of vehicles around the airport that produce greenhouse emissions. In this paper, we outline the methodology used to develop our scale. A total of 365 participants were solicited to help generate items for the scale, narrow down the list of items to those most relevant to a positive experience with intermodal rail, and test the final scale for validity and reliability. A factor analysis using the principle components and varimax rotation loaded strongly on one factor, providing evidence for validity. Reliability was tested via Cronbach’s Alpha and Guttmann’s Split-half tests, indicating high consistency and reliability.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".