A Virtual Standoff – Using Q Methodology to Analyze Virtual Reference
Bibliographic record
Abstract
Abstract Objective - To develop an exploratory understanding of reference librarians’ perceptions of virtual reference. Methods – Q methodology was used to uncover points of view about virtual reference. Thirty-four librarians sorted 28 statements covering a wide range of opinions about virtual reference. Factor analysis was used to analyze the Q-sorts and factor scores were calculated to aid the task of understanding and interpretation. Results - The factor analysis revealed three attitudinal typologies: Technophiles, Traditionalists, and Pragmatists. Each factor represents a group of reference librarians who think similarly about virtual reference. Conclusions - This type of analysis provides data on the actual range of feelings and attitudes about providing virtual reference services. The factor analysis demonstrates that there are still a variety of strongly held viewpoints concerning virtual reference. Convergence towards either acceptance or rejection does not appear to be forthcoming. By using this type of analysis and the resulting data as a basis for decision making, administrators could staff services more efficiently and with the resulting better fit between librarians and their positions, possibly increase morale.
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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 | 0.043 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".