“Feels like you’ve hit the lottery”: Assessing the implementation of a discovery layer tool at Ryerson University
Bibliographic record
Abstract
The research study was initiated to evaluate and assess the web-scale discovery (WSD) service Summon to coincide with its launch at Ryerson University Library in September 2011. The project utilized a mixed methods sequential explanatory strategy and applied an inductive analysis. Quantitative data was gathered with two online questionannaires, followed by a series of focus groups with students for the qualitative phase. The quantitative phase of the study collected over 6,200 survey responses (21% of the university population), with over 420 students indicating interest in participating in a qualitative follow-up (6.7% of the respondents). The survey data showed that most undergraduate students rated Summon highly in ease of use; however, there was a lower satisfaction with the large quantity of, and relevance of search results. Additionally, partiticpants indicated that they used Summon in conjunction with other research tools, such as Google Scholar. In the qualitative phase, small focus groups consisted of a total of 13 participants, allowed the students to express their experiences with Summon in depth. The study has given insight into the role of Summon in terms of undergraduate information-seeking behaviour. Participant feedback revealed potential improvements for Summon at Ryerson and will be useful to other institutions either using or considering the use of similar products. Overall, the results from the study will help to infom Ryerson Library practice surrounding future direction in reference, instruction, and service promotion.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".