Bibliographic record
Abstract
This paper examines the effort devoted to securing interviews with a very wealthy part of the sample for the 2007 Survey of Consumer Finances (SCF). Only about a quarter of the group completed an interview. At the close of the field period, more than a third of this part of the sample was judged by the field staff to be still workable—that is, those cases were neither complete nor final refusals. The evolution of the field work was driven both by the behavior of respondents and the behavior of the field staff. The paper uses the formal data coded in the call records for each case to describe the work. But that information is inconclusive about the factors that drove the work. However, informal notes in the call records do provide a clear picture of the points of resistance among respondents. Although it was difficult to locate, contact, and convince respondents of the legitimacy and value of the survey, it appears that the ultimate constraint in a large proportion of cases was the length of the interview—potentially several hours for these respondents. Examination of the available auxiliary data provides little evidence of nonresponse bias. Acknowledgements
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.966 | 0.949 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".