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Record W7030713658

NTA Mystery Passenger Survey : Direct Award : 2024 : Quarter 1

2024· report· en· W7030713658 on OpenAlexaboutno aff

Bibliographic record

VenueTrinity's Access to Research Output (TARA) (Trinity College Dublin) · 2024
Typereport
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Service (business)Measure (data warehouse)Data collectionQuality (philosophy)Public transport
DOInot available

Abstract

fetched live from OpenAlex

This research programme monitors service, quality and compliance with contractual Bus Éireann requirements through “mystery shopping‟ surveys, to measure key aspects of service delivery. This mystery shopping programme was designed to provide robust and actionable data to the National Transport Authority to measure the overall service performance through the eyes of ‘customers’. 392 mystery shops were conducted during Quarter 1 with mystery shoppers acting as passengers while waiting for and on-board selected Bus Éireann routes. A broad spread of bus routes were covered across different days of the week and times of the day. Bus station assessments were also completed in Quarter 1 and the data for these assessments are included in this report. The mystery shops were carried out by trained Ipsos assessors. These assessors use mobile devices, which enable both discreet and effective captures of location, bus and driver details at stops, when boarding, on board and after alighting buses. 2024 Quarter 1 took place between 1st Jan to 24th March 2024.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.054
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0450.025

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 head0.299
GPT teacher head0.484
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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