TREND: Criminal Justice Information Services Division, Federal Bureau of Investigation, Federal Bureau of Investigation. Arrests: Uniform Crime Reporting Population Totals | State: Virginia | County: Amelia, Amherst, Appomattox, Arlington, Augusta, Bath, Bedford, Bedford City, Bland, Botetourt, Bristol, Brunswick, Buchanan, Buckingham, Buena Vista City, Campbell, Caroline, Carroll, Charles City, Charlotte, Charlottesville City, Chesapeake City, Chesterfield, Clarke, Clifton Forge City, Colonial Heights City, Covington City, Craig, Culpeper, Cumberland, Danville City, Dickenson, Dinwiddie, Emporia City, Essex, Fairfax, Fairfax City, Falls Church City, Fauquier, Floyd, Fluvanna, Franklin, Franklin City, Frederick, Fredericksburg City, Galax City, Giles, Gloucester, Goochland, Grayson, Greene, Greensville, Halifax, Hampton City, Hanover, Harrisonburg City, Henrico, Henry, Highland, Hopewell City, Isle Of Wight, James City, King And Queen, King George, King William, Lancaster, Lee, Lexington City, Loudoun, Louisa, Lunenburg, Lynchburg City, Madison, Manassas City, Manassas Park City, Martinsville City, Mathews, Mecklenburg, Middlesex, Montgomery, Nelson, New Kent, Newport News City, Norfolk City, Northampton, Northumberland, Norton City, Nottoway, Orange, Page, Patrick, Petersburg City, Pittsylvania, Poquoson City, Portsmouth City, Powhatan, Prince Edward, Prince George, Prince William, Pulaski, Radford, Rappahannock, Richmond, Richmond City, Roanoke, Roanoke City, Rockbridge, Rockingham, Russell, Salem, Scott, Shenandoah, Smyth, Southampton, Spotsylvania, Stafford, Staunton City, Suffolk City, Surry, Sussex, Tazewell, Virginia Beach City, Warren, Washington, Waynesboro City, Westmoreland, Williamsburg City, Winchester City, Wise, Wythe, York, 2016. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 010-002-003
Bibliographic record
Abstract
Criminal Justice Information Services Division, Federal Bureau of Investigation, Federal Bureau of Investigation. Arrests: Uniform Crime Reporting Population Totals | State: Virginia | County: Amelia, Amherst, Appomattox, Arlington, Augusta, Bath, Bedford, Bedford City, Bland, Botetourt, Bristol, Brunswick, Buchanan, Buckingham, Buena Vista City, Campbell, Caroline, Carroll, Charles City, Charlotte, Charlottesville City, Chesapeake City, Chesterfield, Clarke, Clifton Forge City, Colonial Heights City, Covington City, Craig, Culpeper, Cumberland, Danville City, Dickenson, Dinwiddie, Emporia City, Essex, Fairfax, Fairfax City, Falls Church City, Fauquier, Floyd, Fluvanna, Franklin, Franklin City, Frederick, Fredericksburg City, Galax City, Giles, Gloucester, Goochland, Grayson, Greene, Greensville, Halifax, Hampton City, Hanover, Harrisonburg City, Henrico, Henry, Highland, Hopewell City, Isle Of Wight, James City, King And Queen, King George, King William, Lancaster, Lee, Lexington City, Loudoun, Louisa, Lunenburg, Lynchburg City, Madison, Manassas City, Manassas Park City, Martinsville City, Mathews, Mecklenburg, Middlesex, Montgomery, Nelson, New Kent, Newport News City, Norfolk City, Northampton, Northumberland, Norton City, Nottoway, Orange, Page, Patrick, Petersburg City, Pittsylvania, Poquoson City, Portsmouth City, Powhatan, Prince Edward, Prince George, Prince William, Pulaski, Radford, Rappahannock, Richmond, Richmond City, Roanoke, Roanoke City, Rockbridge, Rockingham, Russell, Salem, Scott, Shenandoah, Smyth, Southampton, Spotsylvania, Stafford, Staunton City, Suffolk City, Surry, Sussex, Tazewell, Virginia Beach City, Warren, Washington, Waynesboro City, Westmoreland, Williamsburg City, Winchester City, Wise, Wythe, York, 2016. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 010-002-003 Dataset: Present population totals as provided by the FBI. See the technical documentation for information on the process used by the FBI in estimating totals. Shows arrests, by offense, state, county, jurisdiction, and suspect age, sex, and race. Data are from the Uniform Crime Reporting (UCR) Program, which is a nationwide, cooperative statistical effort of more than 17,000 city, university and college, county, state, tribal, and federal law enforcement agencies voluntarily reporting data on crimes brought to their attention. The UCR Program counts one arrest for each separate instance in which a person is arrested, cited, or summoned for an offense. Because a person may be arrested multiple times during the year, the UCR arrest figures do not reflect the number of individuals who have been arrested. Rather, the arrest data show the number of times that persons are arrested, as reported by law enforcement agencies to the UCR Program. The UCR system defines a "juvenile" as anyone under 18 years of age, regardless of state definitions. https://ucr.fbi.gov/word Category: Criminal Justice and Law Enforcement Subject: Population Source: Federal Bureau of Investigation The Federal Bureau of Investigations (FBI) is the principal investigative arm of the U.S. Department of Justice. The Uniform Crime Reporting (UCR) Program was conceived in 1929 by the International Association of Chiefs of Police to meet a need for reliable, uniform crime statistics for the nation. In 1930, the FBI was tasked with collecting, publishing, and archiving those statistics. A 5-year redesign effort to provide more comprehensive and detailed crime statistics resulted in the National Incident-Based Reporting System (NIBRS) which collects data on each reported crime incident. The UCR Program is currently being expanded to NIBRS. http://www.fbi.gov/
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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.175 | 0.177 |
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".