Spatial cyberinfrastructures, ontologies, and the humanities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We report on research into building a cyberinfrastructure for Chinese biographical and geographic data. Our cyberinfrastructure contains (i) the McGill-Harvard-Yenching Library Ming Qing Women's Writings database (MQWW), the only online database on historical Chinese women's writings, (ii) the China Biographical Database, the authority for Chinese historical people, and (iii) the China Historical Geographical Information System, one of the first historical geographic information systems. Key to this integration is that linked databases retain separate identities as bases of knowledge, while they possess sufficient semantic interoperability to allow for multidatabase concepts and to support cross-database queries on an ad hoc basis. Computational ontologies create underlying semantics for database access. This paper focuses on the spatial component in a humanities cyberinfrastructure, which includes issues of conflicting data, heterogeneous data models, disambiguation, and geographic scale. First, we describe the methodology for integrating the databases. Then we detail the system architecture, which includes a tier of ontologies and schema. We describe the user interface and applications that allow for cross-database queries. For instance, users should be able to analyze the data, examine hypotheses on spatial and temporal relationships, and generate historical maps with datasets from MQWW for research, teaching, and publication on Chinese women writers, their familial relations, publishing venues, and the literary and social communities. Last, we discuss the social side of cyberinfrastructure development, as people are considered to be as critical as the technical components for its success.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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 it