Detroit Metro Area Communities Study (DMACS) Wave 12, Michigan, 2021
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 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 it