Detroit Metro Area Communities Study (DMACS) Wave 12, Michigan, 2021
Bibliographic record
Abstract
The Detroit Metro Area Communities Study (DMACS) is a panel survey of Detroit residents launched in 2016. The original panel of respondents was drawn from an address-based probability sample of all occupied Detroit households. In subsequent years, the panel has been refreshed through additional address-based sampling. The 12th survey wave, collected between January 6, 2021 and March 5, 2021 included a sample refresh using multiple recruitment modes (mail, email, text, and phone). The researchers sent a total of 11,655 invitations to the survey: 1,766 to existing DMACS panelists who had already responded to at least one prior survey and 9,889 to residents of a randomly-selected address-based refreshment sample of Detroit households. This refreshment included an oversample of households in Census block groups that were at least 70% Hispanic and households in Strategic Neighborhood Fund (SNF) neighborhoods. Surveys were self-administered online or interviewer-administered via telephone. Adaptive design was used to increase response rates amongst hard-to-reach subgroups. The researchers report results for the 2,238 Detroit residents who completed the survey. The researchers obtained an overall response rate of 20.22% (using American Association for Public Opinion Research (AAPOR) Response Rate 1); 72.6% for existing panelists and 10.4% for new panelists.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.008 | 0.003 |
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.
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".