Communication Breakdown: Librarian and Student Approaches to Virtual Reference Differ
Bibliographic record
Abstract
A review of: Walter, Virginia A. and Cindy Mediavilla. "Teens Are from Neptune, Librarians Are from Pluto: An Analysis of Online Reference Transactions.” Library Trends 54.2 (2005): 209-227. Objective – To evaluate the effectiveness of an online reference and referral service for students (primarily those in middle school and high school) seeking homework help. Design – Analysis of 114 transcripts of reference transactions. Setting – A centralized homework reference and tutor referral service provided on behalf of the California State Library by the Metropolitan Cooperative Library System. Subjects – Virtual reference librarians at a large urban library system and middle and high school students in California. Methods – One hundred fourteen virtual reference transactions recorded between October 12 and November 8, 2003 were evaluated against the Reference and User Services Association’s (RUSA) “Guidelines for Behavioral Performance of Reference and Information Service Providers.” Secondly, the transcripts were subjected to discourse analysis. Main results – Performance of virtual reference librarians vis-à-vis RUSA guidelines In the majority of cases, there was some evidence that librarians communicated clearly (110 out of 114 transactions). In 78 cases, it appeared that a librarian was available quickly, and in 92 of the transactions a friendly greeting was given. What was striking, however, was that in a clear majority of cases, some of the classic reference interview strategies were not employed. In 100 or more cases each, the following strategies were not observed: repeating or paraphrasing the question; helping to interpret the question; verifying mutual understanding; asking if the question has been answered; asking if the student needs more information. Furthermore, in 75 cases librarians did not probe for further information to clarify the question, while in 87 cases they did not check that information had been clearly understood. Possibly related to these findings, the researchers felt that the transcripts revealed “a conviction that homework questions are not the proper content for reference transactions” (222). In addition, librarians were found to be frequently too quick to refer students to a tutor, when a query might have been better answered as a reference question. Findings of discourse analysis In general, the virtual reference librarians used impersonal, formal language to “reinforce the professional’s superior position vis-à-vis the help-seeker” (217). There were repeated attempts by the students to interject a lighter or warmer tone (using humour, emoticons, informal language, introducing a personal note, etc.). These attempts were rarely reciprocated, with librarians continuing to use impersonal language, including stock messages such as: “we are experiencing a very busy time right now,” (217); or, “I am going to send you a page which will give you some help with your homework. After we disconnect this session, click on this link and follow the instructions to be connected with a tutor. Please do not click on any links on this page until after we have disconnected” (217). In several cases librarians were preoccupied with defining their roles—for example, specifying that they could give reference help but not advice. In all, the distancing mechanisms used by librarians, combined with occasional inaccurate referrals and technical problems with the software, were seen to create an enormous potential for frustration on the part of the student. Conclusions – The most important implication of the study was that librarians and students were worlds (or planets) apart in their approach to the reference interaction. While “teens attempted to create meaning by recreating the chat discourse environment in which they were most at home”, librarians “tried to create meaning in a parallel discourse environment that duplicated as much as possible the standard impersonal protocols of a face-to-face reference counter” (223). One suggested way to alleviate the disconnect between librarians and students was to involve students in the planning of the services. For now, however, the authors conclude that “teens are from Neptune, librarians are from Pluto. Better services would result if they could meet somewhere closer together” (224).
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".