Miscommunication and Employee Power Dynamics May Affect Student Navigation of Library Resources
Bibliographic record
Abstract
A Review of: Almeida, N., & Tidal, J. (2022). Library wayfinding and ESOL students: Communication challenges and empathy-based intervention. portal: Libraries and the Academy, 22(2), 453–474. https://doi.org/10.1353/pla.2022.0025 Objective – To map the experiences of students of English for speakers of other languages (ESOL) navigating an academic library. Design – A wayfinding study to evaluate how students navigate a library. Setting – An urban-based academic library at an institution of higher education. Subjects – Students of English for speakers of other languages (ESOL). Methods – A mixed methods study including visual recordings, web screen capture, interviews, and surveys. Subjects were recruited through email. Twelve participants were selected and given an initial screening survey. They were given four tasks to complete: Find a book in the stacks, find a book in the reserves, find a DVD in media, and find a database. They were equipped with a GoPro camera and were given a think-aloud protocol (TAP). They were then given a post-task debriefing interview. Qualitative data were analyzed and coded. Quantitative data like success of task and time to completion were also recorded. Main Results – Success rate varied among tasks: Finding a book in reserves had the highest rate at 75%, while finding a database had the lowest at 50%. Time also varied from 12 minutes to find a book in the stacks to just under 6 minutes to find a database. Seven of the 12 participants indicated they had prior library experience; however, they still encountered skill gaps. They lacked familiarity with the space, policies, website, and terminology. Participants also struggled with library jargon and inconsistent use of jargon among staff and librarians. Conclusion – The researchers discovered there were discrepancies between language used in signs, directions provided by staff, and information provided on the website. Signage was important because several participants made remarks on lack a familiarity with the library space. They would get lost and anxious. In addition, the video recordings and subsequent discussions among the staff and librarians showed issues arising from the power dynamics in the library organization. Staff felt pressured to provide reference services when librarians were unavailable due to staffing shortages, which led to miscommunication. These conclusions lead to empathy-based training to address language discrepancies and experiences among staff. It also provided additional rationale for hiring.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".