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Record W2604981585 · doi:10.1177/0049124117701484

The Relationship Between the Position of Name Generator Questions and Responsiveness in Multiple Name Generator Surveys

2017· article· en· W2604981585 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSociological Methods & Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSurvey Methodology and Nonresponse
Canadian institutionsImpactMcMaster UniversityPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsSatisficingPsychologySocial psychologyGenerator (circuit theory)Position (finance)Computer scienceArtificial intelligenceBusinessPower (physics)

Abstract

fetched live from OpenAlex

Using randomly ordered name generators, we tested the effect of name generators’ relative position on the likelihood of respondents’ declining to respond or satisficing in their response. An online survey of public health staff elicited names of information sources, information seekers, perceived experts, and friends. Results show that when name generators are asked later, they are more likely to go unanswered and respondents are more likely to respond that they do not know anyone or list fewer names. The effect of sequence was not consistent in different question types, which could be the result of the moderating effect of willingness to answer and question sensitivity.

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.

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)0.390
metaresearch head score (Gemma)0.427
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.3900.427
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.007
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.704
GPT teacher head0.622
Teacher spread0.082 · 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