Bibliographic record
Abstract
Linked data is the language of the web, and is a profoundly important opportunity for libraries to expand their reach. This talk will highlight how University of Alberta and University of Pennsylvania are leveraging key community partnerships (Share-VDE, LD4, PCC, Wikidata) and institutional strategic objectives to drive implementation choices, presenting generalizable approaches to successful linked data strategies in libraries. A general linked data vision takes a holistic approach to library implementation by supporting overall library strategic goals and objectives. Linked data objectives are not a separate or compartmentalized goal, but rather the overarching long term effects of linked data support library operations. Indeed, as library linked open data have proliferated on the web, linking external datasets with classic sources of library information have led to increased exposure of library resources, collections, and expertise. This talk will contextualize linked data from the perspective of current systems; extensions that can be made from where any library is at; and from any level of resource constraint. It is often possible to include linked data in more traditional representations, or to make connections between linked data and more familiar formats. This presentation will introduce examples of these mixed-format or “hybrid” linked data environments as they are expected to be the most common way in which linked data is used in production in the next few years. For the foreseeable future, linked data strategy is based on experimentation, innovation, and collaboration. This will encompass work in a hybrid environment that will include MARC records, sometimes enhanced with linked data identifiers, as well as more “native” linked data formats.
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 | 0.062 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.017 | 0.026 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.047 | 0.051 |
| Open science | 0.013 | 0.034 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".