Northeast Region Household Data. NER-Stat: Caregiving Survey
Bibliographic record
Abstract
The NER-Stat: Caregiving Survey is the regional household survey NCRCRD conducted in collaboration with Ohio State University and Pennsylvania State University. It is a 15-minute survey focusing solely on households in the Northeast Region (NER) and asks questions about household demographics, education, non-caregiving, and child, adult, and elderly caregiving. The primary purpose of this survey is to learn more about individuals and families who provide care and how caregiving affects economic development and quality of life in the Northeast Region. All data gathered via the NER-Stat: Caregiving Survey are available for those who want to use the data as a baseline for further research and extend the portfolio of already existing databases. The information gathered from this survey is intended to be shared with communities, organizations, and decision-makers to help inform future policies and programs to support better caregiving and caregivers in the Northeast Region. The survey was designed as an online survey using Qualtrics. Qualtrics® distributed the survey and gathered data based on pre-defined sampling quotas and screening questions. The goal was to maximize participation in the survey throughout the states, across rural and urban areas, household types, race and ethnicity, age groups, and gender. The dataset includes household data from all states in the NER: Connecticut, Delaware, the District of Columbia, Maine, Maryland, Massachusetts, New Jersey, New Hampshire, New York, Pennsylvania, Rhode Island, Vermont, and West Virginia. The final number of respondents is 4,480, of which 725 respondents only cared for a child/children, 714 respondents only cared for an adult(s), and 1,175 respondents cared for a child/children and adult(s).
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.081 | 0.041 |
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".