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Record W2125692600

The Ethical Challenges and Professional Responses of Travel Demand Forecasters

2003· article· en· W2125692600 on OpenAlexaboutno aff
P. Anthony Brinkman

Bibliographic record

VenueeScholarship (California Digital Library) · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
FundersUniversity of California Transportation CenterNational Science Foundation
KeywordsExcuseSuspectFeelingField (mathematics)Work (physics)Public relationsEconomicsPolitical scienceMarketingBusinessPsychologySocial psychologyLawEngineering
DOInot available

Abstract

fetched live from OpenAlex

Thirty years ago scholars first presented convincing evidence that local officialsuse biased travel demand forecasts to justify decisions based on unstated considerations.Since then, a number of researchers have demonstrated convincingly that such forecastsare systematically optimistic–often wildly so–for reasons that cannot be explained solelyby the inherent difficulty of predicting the future. Why do modelers–professional engineers and planners who use quantitative techniques to predict future demand for traveland estimate its potential impact on built and proposed transportation facilities–generatebiased forecasts and otherwise tolerate the misuse of their work? On initial consideration, it is tempting to surmise that corrupt modelers are responsible for biased forecasting.Indeed, corruption is the most common explanation of forecasting bias and tales of mercenary behavior are all too common in the field. Data from in-depth interviews withtwenty-nine travel demand forecasters throughout the United States and Canada, how-2ever, suggest new and different ways to understand the suspect behavior of transportationplanning professionals.Those most likely to introduce bias and invite misuse of travel forecasts assumethat their technical analyses have little, if any, impact on policy making. For many, thisleads to disillusionment and requires responses to cope with feelings of marginalization.Others, untroubled by their apparent lack of influence, are complacent and need ways toavoid the ethical questions of practice. Both types of practitioners circumscribe professional roles and rely on the self-deceptive strategies of evasion and excuse making tomute 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 andmisuse seem to be the unintentional byproducts of these attitudes.Beyond enhancing the understanding of the systemic failures of travel demandmodeling, this research suggests practicable steps to reform and outlines an agenda forfuture work. Attention to these matters is important, not just to avoid expenditures onprojects and programs that cannot be justified on the basis of sound utilitarian calculations, but also to restore and preserve the credibility of a profession.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.271
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2003
Admission routes1
Has abstractyes

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