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 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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".