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Record W584676618

MARKETING MISTAKES CAN LEAD TO FAILURE IN YOUR P-AND-D INSTALLATION

2004· article· en· W584676618 on OpenAlexaboutno aff
S Campbell

Bibliographic record

VenueParking Today · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsPaymentRevenueBusinessKey (lock)MarketingPublic relationsAdvertisingComputer securityComputer scienceFinancePolitical science
DOInot available

Abstract

fetched live from OpenAlex

This article analyzes key issues needed to be addressed in successful adoption of a pay-and-display (p-and-d) parking payment system. It examines the experiences of Saskatchewan Provincial Parks in implementing a p-and-d system on a trial basis at 6 of its 34 parks. The internal working group was pleased with the results, but the public reaction was mixed, with some customers extremely angry at the system. The new system was designed to raise revenues by making sure visitors paid for parking to the full extent they used it. Key elements that needed improving were: an integrated communications strategy launched well in advance to educate the public about the new system; use of free media coverage to spread word about the change; better location of pay stations; easier-to-read and -understand signs; proactive communications and development of concise answers to FAQs; and placing staff near the new machines for the early period of the introduction.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.029
GPT teacher head0.251
Teacher spread0.222 · 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 designObservational
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

Citations0
Published2004
Admission routes1
Has abstractyes

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