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Record W7100922193

Recall Period in the Consumer Expenditure Surveys Program 1. Background Statement

2013· article· en· W7100922193 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicComplex Systems and Time Series Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsRecallForgettingAsk priceRecall testSerial position effectCognitionFree recallQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

Memory studies consistently demonstrate that recent events are recalled more accurately than events occurring further in the past (Groves 1989), that memory decay increases with longer recall periods, and that memory decay is greater for less salient events (Silberstein 1989). The resulting recall error, or misreporting of events due to problems in recall, may stem from both errors of omission, such as the simple forgetting of events, as well as errors of commission, such as misreporting due to telescoping events from an earlier or later period into the recall period. The CEQ currently employs a three-month recall period. The length of this three-month recall period, combined with the wide range of question types asked, is generally thought to represent a substantial cognitive burden for respondents. Furthermore, there are different approaches to asking about the three-month recall period, which may compound the cognitive burden for respondents. For example, some CEQ questions ask about cumulative expenses over the entire three-month recall period, other questions ask respondents about total monthly expenditures for the first, second, and third month of the recall period, and still others ask respondents for average weekly expenses over the recall period. As Mathiowetz (1987) summarizes, these variations in the reference period require that respondents search their

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.998

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)0.0170.003

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 head0.056
GPT teacher head0.259
Teacher spread0.202 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2013
Admission routes1
Has abstractyes

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