Taxi Tipping in New York City (2014-2017): Reciprocity in Hailed vs. Dispatched Cab Fares
Bibliographic record
Abstract
The present study evaluated the extent to which reciprocity (equity) theory could explain differential levels of tipping in New York taxi fares. From 2014 to 2017, the database recorded 73 million cab fares; however, only credit transactions (i.e., recording patrons’ tips) were included (28 million fares). Based on a reciprocity hypothesis, patrons in cabs hailed randomly off the street were expected to tip more compared to patrons who arranged travel at a dispatch centre. An analysis of covariance for each of the four years supported this, wherein patrons in hailed cabs tipped twice the percentage (14%) than patrons in dispatched cabs (7%); these results were confirmed using equivalent procedures that assumed neither normality nor variance homogeneity. Several limitations are discussed, as are directions for future research. Keywords: reciprocity, tipping, equity, genuine intention, taxi
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".