Bibliographic record
Abstract
ABSTRACT When a serendipitous discovery is made while searching within a digital library collection (such as an academic digital library at a university), the searcher has a difficult choice to make: either pursue the serendipitous discovery or set it aside and deal with it later. If they take the first option, this breaks the flow of the primary search activity which may make it difficult to resume. If they take the second option, they may have difficulty re‐finding what they discovered when they are finished with the primary search activity. We have developed a novel search interface that includes topic‐based workspaces and a “read it later” list. Serendipitous discoveries can be easily added to the “read it later” list, allowing the searcher to stay focused on their current search activity knowing that they can easily return to the discovered resource. For each resource saved to the “read it later” list, a textual similarity is calculated against the collection of documents saved in each of the searcher's workspaces. This allows them to easily identify which of their prior search tasks is a best fit for the discovery, as well as an ability to create a new workspace if desired.
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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".