Grey is the new black: Changing library instruction virtually
Bibliographic record
Abstract
Searching for grey literature can often be a tricky and overwhelming process, in part because this topic is not always integrated into standard information literacy teaching sessions in post-secondary libraries (Mahood 2014, p.222). Despite the fact that grey literature is not considered scholarly, it is an important source of information for students, researchers and professionals in different areas of study and employment. This topic is often overlooked and not always integrated into standard information literacy teaching sessions in post-secondary libraries. And it should be. As Kingsley suggests, “the role libraries hold within research institutions is changing as the world shifts towards a digital and increasingly open future. This requires a rethink of the types of services and skill sets that are appropriate for an academic library to encompass” and teach (Kingsley, 2020, p.281). While graduate students are well versed in searching for traditional academic literature (e.g., monographs and peer reviewed journal articles), they might be a bit “out of their league” in their ability to find grey literature (e.g., literature published outside of the mainstream academic and commercial publishing sectors). However, grey should be their new black— for example, many masters and doctoral students need to be equally capable of finding the material published by a wide range of researchers if they are to graduate with a robust set of professional skills that can be used in a variety of workplace contexts. Our experience in designing and delivering a grey literature workshop tells us that our instincts are accurate—students are eager to learn these skills and put them to use in either a face-to-face or a virtual classroom.
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.
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 | 0.030 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.027 | 0.038 |
| Open science | 0.005 | 0.042 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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".