Cultural Heritage Informatics, Old Idea or Emerging Domain?
Bibliographic record
Abstract
This is a 90-minute roundtable discussion, moderated by Shirin G. Alamdari. With Hannah Turner (UBC); Stacy Allison-Cassin (Dalhousie), Isto Huvila (UU), Andrea Thomer (UArizona) and Diana Marsh (UMD Maryland). The term, Cultural Heritage Informatics, is being used widely in Information fields and by Information Studies Scholars and programs. This panel will address the questions: What is Cultural Heritage Informatics? Why do researchers and institutions stumble with a definition? Does Cultural Heritage Informatics define a methodology, a subject interest, or a set of technical practices? What kinds of ethical considerations could we, or should we, have?
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".