Trend 2009 - 2013. National Center for Education Statistics. Public Elementary/Secondary School Census Detail: Student Count - High Schools | Country: USA | State: Washington | County: Clark | Education Agency: VANCOUVER SCHOOL DISTRICT | School: VANCOUVER INTERNET CONNECTION | Grade Level: All Grades | Gender: All Gender | Race/Ethnicity: All Races, 2009-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 017-001-014.
Bibliographic record
Abstract
National Center for Education Statistics (2016). Public Elementary/Secondary School Census Detail: Student Count - High Schools | Country: USA | State: Washington | County: Clark | Education Agency: VANCOUVER SCHOOL DISTRICT | School: VANCOUVER INTERNET CONNECTION | Grade Level: All Grades | Gender: All Gender | Race/Ethnicity: All Races, 2009-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 017-001-014. Dataset: Annual headcount of high school students. High schools are schools offering a low grade of 7 or higher and a high grade of 12. Data are from the Common Core of Data (CCD), a program of the United States Department of Education's National Center for Education Statistics that annually collects fiscal and other data about all public schools, public school districts, and state education agencies in the US. The data are supplied by state education agency officials. For all data sets, FIPS codes for the school facility are provided. County and state numbers are calculated based on the given FIPS codes, and national numbers are created as a roll-up of the detailed numbers. Also, All Grades All Races and All Gender are created by rolling up the appropriate, given detail data. NCES datasets dates are labeled using a two-year notation specifying the start year and the end year; eg, 2006-2007. Data-Planet uses the start year to note the time period covered, eg, 2006 for the 2006-2007 school year. Category: Education Source: National Center for Education Statistics The National Center for Education Statistics (NCES) is the primary federal entity in the United States for collecting and analyzing data related to education in the US and other nations. NCES is located within the US Department of Education and the Institute of Education Sciences. The NCES fulfills a congressional mandate to collect, collate, analyze, and report complete statistics on the condition of US education; conduct and publish reports; and review and report on education activities internationally. The NCES is one of four centers (along with the National Center for Education Research, the National Center for Education Evaluation and Regional Assistance, and the National Center for Special Education Research) charged with carrying out the work of the Institute of Education Sciences. http://nces.ed.gov/ Subject: Education Enrollment, High Schools, Secondary School Students, High School Students
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.021 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.090 | 0.093 |
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".