Looking for Information that is not Easy to Find: An Inventory of LibGuides in Canadian Post-Secondary Institutions Devoted to Grey Literature
Bibliographic record
Abstract
In order to obtain a representative sample of the use of grey literature in LibGuides across Canadian post-secondary institutions, an environmental scan was undertaken, identifying 17 colleges or universities where grey literature resources were directly mentioned and included alongside academic databases. After viewing the LibGuides within each of the post-secondary institutions listed in Table 2 of the attached paper, 52 library staff (librarians and information specialists) were identified. A brief online survey (please see accompanying dataset file) was sent to each of the 52 library staff members, to uncover how students and researchers use grey literature, and perhaps most importantly, to verify from the participant responses whether or not sections of existing LibGuides have been devoted to including the grey literature in information-seeking pursuits. 9 of the in 17 institutions polled participated in the survey, yielding a response rate of 52.9%. All respondents confirmed that grey literature was mentioned in the research guides/subject guides/LibGuides used within their institution. This data set is affiliated with GL18, the 18th International Grey Conference, held at the New York Academy of Medicine from November 28-29, 2016. The presentation slides were delivered at GL 18 and were published in the GL18 Conference Book, produced by GreyNet. The accompanying full-text paper will be published by GreyNet in the GL18 Conference Proceedings, scheduled for release in February 2017.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.038 | 0.051 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".