Developing a Weighted Collection Development Allocation Formula
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this preconference workshop Bailey, Creibaum, and Holloway presented detailed instructions on how to create a spreadsheet-based library collection development allocation formula, one option to manage a library’s collection development budget. The presenters demonstrated and led participants through the process of creating customizable Excel-based formulas that can easily be modified to utilize the criteria relevant to a specific library and institution. The primary element in the success of such a formula is the use of weights applied to each factor contained in the spreadsheet. Potential factors include the number of students graduating from each degree program, total faculty per department, departmental credit hour production, the number of courses offered, and the average costs of books and journals in a discipline. By carefully assigning weights to each factor, the output of the formula results in an equitable allocation of funds to each subject area.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it