Bibliographic record
Abstract
ABSTRACT In your research and daily life, you may have primarily relied on Google as your primary search engine. This general search tool may have successfully fulfilled your information needs thus far. However, as you advance through your studies, you reach a stage in your education at which you are expected to find and report on substantive, scholarly research conducted by professionals in a field, and Google no longer suffices. The sources for this type of scholarly information are peer-reviewed journals, books published by academic and university presses, and formal reports issued by government agencies. But these are not the type of sources that are accessed in a basic Google search. Therefore, search engines like Google are usually inadequate for the tasks you will be expected to accomplish. So, if you can't “just Google it,” how do you conduct research at this new level? One answer is the library!
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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.019 | 0.034 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.034 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.349 | 0.255 |
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".