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Record W3099721341 · doi:10.3886/e120453v1

2020 Region of Waterloo Survey

2020· dataset· en· W3099721341 on OpenAlexaff
Anthony Piscitelli

Bibliographic record

VenueICPSR Data Holdings · 2020
Typedataset
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsConestoga College
Fundersnot available
KeywordsTransport engineeringComputer scienceGeographyEngineering

Abstract

fetched live from OpenAlex

The Interactive Voice Response (IVR) survey was conducted by Professor Anthony Piscitelli with assistance from students at Conestoga College. The survey was funded by the Region of Waterloo. This survey was conducted by randomly sampling listed landline numbers and randomly dialing likely Waterloo Region cellphone numbers. Business numbers were removed from the sample of phone numbers before dialing. esults are considered accurate +/- 3.9%, 19 times out of 20. The margin of error on subsamples is higher. Sampling error also exists due to the mismatch created by the random dialing of phone numbers as opposed to random sampling actual Waterloo Region residents.<br>

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 categoriesMeta-epidemiology (narrow), Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0080.006
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 head0.075
GPT teacher head0.266
Teacher spread0.191 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2020
Admission routes1
Has abstractyes

Explore more

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