MétaCan
Menu
Back to cohort
Record W6928637813 · doi:10.3886/icpsr38199

Detroit Metro Area Communities Study (DMACS) Wave 12, Michigan, 2021

2022· dataset· en· W6928637813 on OpenAlexaboutno aff

Bibliographic record

VenueICPSR Data Holdings · 2022
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
FundersJohn S. and James L. Knight Foundation
KeywordsCensusSample (material)American Community SurveyPanel surveyQuarter (Canadian coin)Public opinionData collectionSurvey research

Abstract

fetched live from OpenAlex

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.140
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0190.032
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.1450.005

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.

Opus teacher head0.165
GPT teacher head0.324
Teacher spread0.159 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueICPSR Data HoldingsFrench-language works237,207