TREND: National Center for Education Statistics. Institutional Finance - Postsecondary Education: Balance Sheet by Line Item - Private Not-For-Profit Institutions | State: Maryland | Educational Institution: Johns Hopkins University, Loyola University Maryland, Stevenson University, McDaniel College, Notre Dame of Maryland University, Mount St. Mary's University, Washington College, St. John's College | Finance Line Item*: F2H02 | Finance Line Item: Value of endowment assets at the end of the fiscal year, 2004 - 2016. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 017-007-002
Bibliographic record
Abstract
National Center for Education Statistics. Institutional Finance - Postsecondary Education: Balance Sheet by Line Item - Private Not-For-Profit Institutions | State: Maryland | Educational Institution: Johns Hopkins University, Loyola University Maryland, Stevenson University, McDaniel College, Notre Dame of Maryland University, Mount St. Mary's University, Washington College, St. John's College | Finance Line Item*: F2H02 | Finance Line Item: Value of endowment assets at the end of the fiscal year, 2004 - 2016. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 017-007-002 Dataset: Provides finance line item data for private not-for-profit postsecondary institutions in the United States. Specific data elements include such items as institutional revenues by source (eg, tuition and fees, government, private gifts); institutional expenditures by function (eg, instruction, research, plant maintenance and operation); physical plant assets and indebtedness; and endowment investments. 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. This annual component of IPEDS collects data that describe the financial condition of postsecondary education in the nation. These data are used to monitor changes in postsecondary education finance and to promote research involving institutional financial resources and expenditures. Note that reporting requirements for private and public institutions differ, and data availability may vary by year. http://nces.ed.gov/IPEDS/ Category: Education Subject: Higher Education, Expenditures, Finances 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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.022 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.145 | 0.140 |
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".