Making publics 2.0: humanities data linked through a topical repository
Bibliographic record
Abstract
Our previous project, Making Publics (MaPs http://www.makingpublics.org), examined voluntary forms of publics in early modern Europe 1500--1700 [1] and resulted in the creation of a corpus of databases of people, places, and artifacts. These databases catalogue the nearly 2,000 works cited in over 200 articles, papers, books, and essays written by its members. These works contain numerous references to people, places, things, and their interactions on various topics, such as common interests, tastes, and desires in early modern Europe. MaPs allows its users to continue this research in public making through collaboration, collating new references to people, places, things that make up the forms of association around shared interests and topics. Throughout 2012, we re-envisaged how the website might operate to further research as well as present the research findings of the project which ended in 2010. Central to this was consideration of how a web application might serve to both organize and foster ongoing research, and make that research data available to other humanities users in the form of Linked Open Data.
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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.026 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.029 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.020 |
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".