Census of Population and Housing: Virgin Islands of the United States Demographic Profile Summary File, 2010
Bibliographic record
Abstract
The Virgin Islands of the United States Demographic Profile Summary File contains data on population and housing subjects derived from questions on the 2010 United States Virgin Islands Census questionnaire. Population subjects include age, sex, children ever born, citizenship status, disability status, educational attainment, race, Hispanic or Latino origin, family type, grandparents as caregivers, group quarters population, health insurance coverage status, household type and relationship, employment status, work experience, class of worker, industry, occupation, place of work, journey to work, travel time to work, language spoken at home and ability to speak English, marital status, foreign born status, nativity, year of entry, place of birth, parents' place of birth, earnings, income, poverty status, residence in 2009, school enrollment, vocational training and veteran status. Housing subjects include computer ownership, cooking fuel, gross rent, internet service, kitchen facilities, mortgage status, number of rooms, number of bedrooms, occupancy status, occupants per room, plumbing facilities, purchase of water from water vendor, selected monthly owner costs, sewage disposal, source of water, telephone service available, tenure, units in structure, vacancy status, value of home, vehicles available, year householder moved into unit and year structure built. The population and housing data are organized in 115 tables which are presented at five levels of observation: the United States Virgin Islands as a whole, island, census subdistrict, estate and place (town or census designated place). Every table cell is represented by a separate variable. The data are segmented into four data files. One data file contains geographic identification variables and the other three the population and housing variables. Together with the data files, the Census Bureau prepared a codebook and additional documentation, a Microsoft Access database shell, and a HTML-based application for displaying the tables called the Interactive Summary Level Access and Navigation Database (ISLAND). ICPSR provides all of these components, except the codebook, in three ZIP archives. The first archive contains the data files, the second the database shell and additional documentation and the third contains ISLAND. The codebook is provided as a separate file.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".