Bibliographic record
Abstract
Information systems are developed by people with intent—they are designed to help creators and users tell specific stories with data. Within information systems, the often invisible structures of metadata profoundly impact the meaning that can be derived from that data. The Linked Infrastructure for Networked Cultural Scholarship project (LINCS) helps humanities researchers tell stories by using linked open data to convert humanities datasets into organized, interconnected, machine-processable resources. LINCS provides context for online cultural materials, interlinks them, andgrounds them in sources to improve web resources for research. This article describes how the LINCS team is using the shared standards of linked data and especially ontologies—typically unseen yet powerful—to bring meaning mindfully to metadata through structure. The LINCS metadata—comprised of linked open data about cultural artifacts, people, and processes—and the structures that support it must represent multiple, diverse ways of knowing. It needs to enable various means of incorporating contextual data and of telling stories with nuance and context, situated and supported by data structures that reflect and make space for specificities and complexities. As it addresses specificity in each research dataset, LINCS is simultaneously working to balance interoperability, as achieved through a level of generalization, with contextual and domain-specific requirements. The LINCS team’s approach to ontology adoption and use centers on intersectionality, multiplicity, and difference. The question of what meaning the structures being used will bring to the data is as important as what meaning is introduced as a result of linking data together, and the project has built this premise into its decision-making and implementation processes. To convey an understanding of categories and classification as contextually embedded—culturally produced, intersecting, and discursive—the LINCS team frames them not as fixed but as grounds for investigation and starting points for understanding. Metadata structures are as important as vocabularies for producing such meaning.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".