Trend 1998 - 2011. Institute of Museum and Library Services. Public Libraries Survey: Operating Expenditures for all Collection Materials | Country: USA | State: Colorado | Library: LOVELAND PUBLIC LIBRARY, 1998-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 058-001-020.
Bibliographic record
Abstract
Institute of Museum and Library Services (2015). Public Libraries Survey: Operating Expenditures for all Collection Materials | Country: USA | State: Colorado | Library: LOVELAND PUBLIC LIBRARY, 1998-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 058-001-020. Dataset: This indicator is the sum of expenditures for collection materials of all types. This dataset contains data on the resources, finances, and use of the public libraries in the United States presented by fiscal year. Data are collected in an annual survey of over 9,000 public libraries conducted by the Institute of Museum and Library Services. Each public library provided data for a 12-month period covering the fiscal year as defined by the locale. Please note that the National Center for Education Statistics (NCES) conducted the FY2005 and earlier Public Libraries Surveys and produced the data files. Category: Education Source: Institute of Museum and Library Services The Institute of Museum and Library Services provides federal funds to libraries and museums in the United States to support improvements in technology and public services. The Institute was founded in 1996 as authorized under the Museum and Library Services Act. http://www.imls.gov/ Subject: Newspapers, Expenditures, Books, Information and Communications Technology, Public Libraries
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.020 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.060 |
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".