Bibliographic record
Abstract
TENTATIVE TOPICS TO BE INCLUDED: PART I: Overview and Scope of the Problem. Unmet School Infrastructure Funding Need as a Critical Educational Capacity Issue, Faith E. Crampton, University of Wisconsin Milwaukee. Overview of State Funding of School Infrastructure: A Comparison of Funding Levels and Mechanisms, Catherine C. Sielke, University of Georgia. Canadian Approaches to the Financing of School Infrastructure, Vivian Hajnal, University of Saskatchewan. School Infrastructure as an Investment in Human Capital, Barbara Y. LaCost, University of Nebraska, and Terry G. Geske, Louisiana State University. PART II: Current Challenges to Funding of School Infrastructure. Urban School Infrastructure Funding Issues, James C. Cibulka, University of Maryland. Funding School Infrastructure: Rural America's Plight, Jeffrey Maiden, University of Oklahoma. Capital Costs and Higher Education Finance, Mary McKeown Moak, MGT of America, Inc. School Level Issues: Are Administrators Efficient Managers of Capital Funds? Brian O. Brent, University of Rochester. PART III: The Future of School Infrastructure Funding. A Policy Framework for the Funding of School Infrastructure, Lawrence O. Picus, University of Southern California. School Finance Litigation: A Strategy to Address Inequities in School Infrastructure Funding, Deborah A. Verstegen, University of Virginia. Alternative Funding Mechanisms for School Infrastructure, Jewell C. Gould, American Federation of Teachers. Funding Technology vs. Bricks and Mortar: Can We Have It All? Donald R. Tetreault, University of South Carolina. PART IV: Conclusion. Striking a Balance: State, Local, and Federal Responsibility for the Funding of School Infrastructure, David C. Thompson, Kansas State University, and Faith E. Crampton, University of Wisconsin Milwaukee.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.050 | 0.007 |
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".