Predicting Future Information Resource Utilization Under Conditions of Scarcity: The First Cohort Study in Health Sciences Librarianship
Bibliographic record
Abstract
A review of: Postell, William Dosité. “Further Comments on the Mathematical Analysis of Evaluating Scientific Journals.” Bulletin of the Medical Library Association 34.2 (1946): 107-9. Objective – To predict future use of journal titles for making subscription decisions. Design – Retrospective cohort study. Setting – Louisiana State University School of Medicine Library in New Orleans. Subjects – All library users, estimated to consist of primarily faculty members or their designees such as research assistants. Methods – Estelle Brodman’s previous citation analysis and reputational analysis (1944) that produced a list of eleven top-ranked physiology journal titles served as the catalyst for Postell’s retrospective cohort study. Postell compiled data on all checkouts for these specific eleven journal titles in his library for the years 1939 through approximately 1945. Main Results – Postell performed a Spearman rank-difference test on the rankings produced from his own circulation use data in order to compare it against journal title rankings produced from three other sources: (1) citation analysis from the references found in the Annual Review of Physiology based upon a system pioneered in 1927 by Gross and Gross; (2) three leading national physiology journals; and, (3) a reputational analysis list of top-ranked journals provided by the faculty members at the Columbia University College of Physicians and Surgeons Department of Physiology. Postell found a relatively high correlation (.755, with 1.000 equaling a perfect correlation) between his retrospective cohort usage data and the reputational analysis list of top-ranked journals generated by the Columbia faculty members. The two citation analyses performed by Brodman did not correlate as highly with Postell’s results. Conclusion – Brodman previously had questioned the use of citation analysis for journal subscription purchase decisions. Postell’s retrospective cohort study produced further evidence against basing subscription purchases on citation analysis. Postell noted that the citation analysis method “cannot always be relied upon as a valid criterion” for selecting journals in a discipline.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".