“I still like Google”: University student perceptions of searching OPACs and the web
Bibliographic record
Abstract
Abstract This paper reports on an exploratory study of how university students perceive and interact with Web search engines compared to Web‐based OPACs. A qualitative study was conducted involving sixteen students, eight of whom were first‐year undergraduates and eight of whom were graduate students in Library and Information Science. The participants performed searches on Google and on a university OPAC. The interviews and think‐afters revealed that while students were aware of the problems inherent in Web searching and of the many ways in which OPACS are more organized, they generally preferred Web searching. The coding of the data suggests that the reason for this preference lies in psychological factors associated with the comparative ease with which search engines can be used, and system and interface factors which made searching the Web much easier and less confusing. As a result of these factors, students were able to approach even the drawbacks of the Web—its clutter of irrelevant pages and the dubious authority of the results—in an enthusiastic and proactive manner, very different from the passive and ineffectual admiration they expressed for the OPAC. The findings suggest that requirements of good OPAC interface design must be aggressively redefined in the face of new, Web‐based standards of usability.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".