Mapping the spatial pattern of the uncertain data in urban areas: The disadvantaged predict global nonresponse rate in the National Household Survey
Bibliographic record
Abstract
High levels of survey nonresponse potentially produce unreliable data due to the often indeterminable possibility of such data being subject to nonresponse bias. In this paper, spatial patterns of global nonresponse rate are analyzed in order to identify whether systemic bias exists across urban spaces with regard to survey nonresponse. Forward stepwise regression is used in combination with spatial regression analysis to build models enabling the prediction of global nonresponse rates in the voluntary 2011 National Household Survey based on explanatory employment, housing, income, and other variables within 11 Canadian cities. The modelling process underscores the inequity of global nonresponse rates; places with high unemployment, high rates of rental properties, a higher proportion of Aboriginal residents, and lower educational attainment have lower compliance with the voluntary survey. Such a pattern has the potential to dramatically influence the ability of government, non‐governmental organizations, and other service providers to address the needs of residents of such urban areas.
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How this classification was reachedexpand
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 | 0.048 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".