Linking Information Seeking Patterns with Purpose, Use, Value, and Return On Investment of Academic Library Journals
Bibliographic record
Abstract
Objective – To demonstrate the power of the critical incident method in studying the information seeking patterns of university faculty. Methods – Faculty at five U.S. universities participated in a study concerning their information seeking and reading patterns involving scholarly journals. The surveys relied on a critical incident method of asking questions concerning the last journal article read. This method allows analysis of the relationships among the purposes of reading articles, ways in which faculty first learned about the articles, where they obtained them, aspects of their use, and the value or impact of the information read. Results – Results show that journal articles were by far the most used source of the last substantive piece of information used for work. Over half of article readings were from articles provided by libraries (52%, compared with 32.6% from personal subscriptions), and journal articles were the most frequent way faculty became aware of information prior to reading about it (33.9%, compared with 19.4% from informal discussions). Conclusion – This project has shown that articles read for the purpose of research, found by searching, and obtained from the library collections have the highest value to faculty by many measures. Library provided articles save faculty time and effort, which can be quantified using contingent valuation. The return on investment (ROI) for library collections can be calculated by measuring all library costs and establishing the monetary returns to faculty members through contingent valuation. Library journal collections are estimated to have an ROI of between 3.3 and 3.6 to 1.
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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 | 0.008 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".