Think Like A Scholar: How to find free and Open Access scholarly resources
Bibliographic record
Abstract
Over the past few decades, since the birth of the internet, the research outputs from universities, governments, and other organizations have become increasingly accessible to the general public. There are now many routes for individuals to access the same information that professional researchers do. In this collaboration between the University of Guelph Library and Guelph Public Library, Librarians from each institution will explain how you can search and access this information for free. We will discuss databases such as Google Scholar and PubMed, full-text finders like Unpaywall, the copyright exceptions that allow article sharing, and the multitude of information resources that Guelph Public Library offers.
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 | 0.035 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.041 | 0.069 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.096 | 0.123 |
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".