TREND: National Center for Education Statistics. Applications/Admittance - Postsecondary Education: Admitted - Total | State: North Carolina | Educational Institution: North Carolina Central University, 2016 - 2018. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 017-003-004
Bibliographic record
Abstract
National Center for Education Statistics. Applications/Admittance - Postsecondary Education: Admitted - Total | State: North Carolina | Educational Institution: North Carolina Central University, 2016 - 2018. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 017-003-004 Dataset: Reports statistics on applicants who have been granted an official offer to enroll in a postsecondary institution. Data are from the Integrated Postsecondary Education Data System (IPEDS) conducted by the NCES. IPEDS involves annual institution-level data collections. All postsecondary institutions that participate in federal programs providing financial assistance to students are required to report data using a web-based data collection system. Applications and admissions information is NOT collected from institutions with an open admissions policy. Admissions data is reported by the beginning year of the academic year; for example, 2014 represents admissions in fall 2014 for the 2014-15 school year. http://nces.ed.gov/IPEDS/ Category: Education Subject: Graduate Students, Higher Education, Undergraduate Students 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/
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 | 0.003 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.031 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.153 | 0.131 |
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".