Bibliographic record
Abstract
30 years ago scholars first offered compelling evidence of local officials using biased travel demand forecasts (TDF) to justify decisions based on unstated considerations. Since then, many researchers have shown convincingly that TDF are systematically optimistic--often wildly so--for reasons that cannot be explained solely by the inherent difficulty of predicting the future. This paper aims to explore the issue of why modelers generate biased TDF and tolerate the misuse of their work. Data from in-depth interviews with 29 travel demand forecasters throughout the U.S. and Canada suggest new ways for understanding the suspect behavior of transport planning professionals. Those most likely to introduce bias and invite misuse of TDF assume their technical analyses have little, if any, impact on policymaking. For many, this leads to disillusionment and requires responses to cope with feelings of marginalization. Others, untroubled by their apparent lack of influence are complacent and need ways to avoid the ethical questions of practice. Both types of practitioners circumscribe professional roles and rely on the self-deceptive strategies of evasion and excusemaking to mute their own disquieting realities that undermine positive concepts of self. The disillusioned wish not to see that they do not matter and the complacent that they do. Bias and misuse seem to be the unintentional byproducts of these attitudes.
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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 | 0.016 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".