Error Analysis of Sampling Frame in Sample Survey
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.
Bibliographic record
Abstract
Abstract: In our application practice of sample survey, we mostly neglect some non-sampling errors such as sampling frame errors. Actually, the influence of non-sampling errors to the total survey deviation can not be ignored. In view of this topic, this paper briefly discussed the sampling frame errors as non-sampling errors. First a brief review of the sampling frame, together with the type and structure of the sampling frame, is given. Next the distinction between sampling frame errors and sampling errors is made theoretically in general. Then through the analysis of a series of non-random impact factors and the application of corresponding improvements or solutions, the sampling frame errors are reduced or controlled within a certain range. Finally, this paper summed up and sorted out the influencing factors based on the sample units or elements for the sampling frame, and also discussed the problems and solutions. Key words: Sampling Survey; Sampling Frame; Sampling Error; Sampling Frame Error
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.030 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it