Looking for Information that is not Easy to Find: An Inventory of LibGuides in Canadian Post-Secondary Institutions Devoted to Grey Literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it