Recall Period in the Consumer Expenditure Surveys Program 1. Background Statement
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.014 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".