Decision support system for library acquisitions: a framework
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
Purpose Acquisition of books, serials and other educational materials by libraries involves a complex decision process; especially when there are many books to choose from and the resources are meager. Attempts have been made in the past to take decisions concerning library acquisitions using structured information such as cost, availability of funds, and number of copies needed by the library, author and year of publication. The purpose of this research is to provide a framework for the combination of both structured and unstructured information in the library acquisitions decision process. Design/methodology/approach The research methodology involves the design of a knowledge‐based system, which is powered by the classical method of the analytical hierarchy process (AHP), which carries out a pairwise comparison (PWC) of acquisition decision variables. Findings The results of the study show that decision variables involved in library acquisitions can be grouped and hierarchically structured. The application of the pairwise comparison matrix produces eigenvectors that aid in stepwise refinement of the results of the conventional acquisition process in order to achieve some level of optimality in the decision process. Originality/value The framework provided in this study could be useful for library professionals and information scientists as a veritable library decision support tool that applies both structured and unstructured information in the acquisition decision process.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.001 | 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 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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