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Record W2341237175 · doi:10.1177/002204260003000102

Developing Computer Assisted Interviewing (CAI) for the National Household Survey on Drug Abuse

2000· article· en· W2341237175 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Drug Issues · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicSurvey Methodology and Nonresponse
Canadian institutionsnot available
Fundersnot available
KeywordsInterviewSubstance abuseQuarter (Canadian coin)Data collectionPsychologyScope (computer science)Medical educationMedicineComputer sciencePolitical sciencePsychiatrySociologyGeographySocial science

Abstract

fetched live from OpenAlex

The National Household Survey on Drug Abuse (NHSDA) is the nation's primary data source for information on the scope and dimensions of drug abuse in the United States. To improve data collection, the NHSDA was converted to a computer-assisted interview (CAI) format in 1999. This paper reports on the research that was done to guide the conversion. The main focus of the paper is on a large (n=1,982) field experiment that examined different audio computer-assisted self-interviewing (ACASI) procedures in the fourth quarter of 1997. This field experiment showed that ACASI is likely to increase reporting of drug use among youth; that respondents can, on their own, correct inconsistencies in responses that are detected by the computer; and that ACASI eases the response task for poor readers and less well educated respondents.

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)0.050
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0500.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.448
GPT teacher head0.478
Teacher spread0.030 · 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