Still Bound for Disappointment? Another Look at Faculty and Library Journal Collections
Bibliographic record
Abstract
Objective – To examine why faculty members at Columbia University are dissatisfied with the library’s journal collections and to follow up on a previous study that found negative perceptions of journal collections among faculty at Association of Research Libraries (ARL) member institutions in general. Methods – In 2006, Jim Self of the University of Virginia published the results of an analysis of LibQUAL+® survey data for ARL member libraries, focusing on faculty perceptions of journal collections as measured by LibQUAL+® item IC-8: “print and/or electronic journal collections I require for my work.” The current analysis includes data from 21 ARL libraries participating in the LibQUAL+® survey from 2006 through 2009. Notebooks for each library were accessed and reviewed for the Information Control and overall satisfaction scores. At Columbia, the results were used to identify departments with negative adequacy gaps for the IC-8 item. Follow-up phone interviews were conducted with 24 faculty members in these departments, focusing on their minimum expectation for journal collections, their desired expectations, and preferences for print or electronic journals. Results – Analysis of the 2009 LibQUAL+® scores shows that faculty across ARL libraries remain dissatisfied with journal collections. None of the libraries achieved a positive adequacy gap, in which the perceived level of service exceeded minimum expectations. There was no significant change in the adequacy gap for the IC-8 item since 2006, and satisfaction relative to expectations remained consistent, showing neither improvement nor decline. While most of the faculty members interviewed at Columbia stated that the journal collections met their minimum expectations, 15 of 24 reported that the library did not meet their desired level of service in this area. Key issues identified in the interviews included insufficient support from library staff and systems regarding journal acquisition and use, the need for work-arounds for accessing needed journals, problems with search and online access, collection gaps, insufficient backfile coverage, and the desire for a discipline-specific “quick list” to provide access to important journals. Conclusion – The issue of satisfaction with journal collections is complex, and faculty members have little tolerance for faulty systems. The evolution of the electronic journal collections and the inherent access challenges will continue to play a critical role in faculty satisfaction as libraries strive to provide ever-better service.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".