Bibliographic record
Abstract
This paper briefly traces the informational history and contemporary formalization of Cultural Heritage Informatics as a distinct domain within IS/LIS, motivated by recent calls for more critical, ethical, and community-oriented interrogations of IS/LIS resulting from recent debates on our professional identity. We unpack the conceptual history and disciplinary intellectual geography of “cultural heritage” and “informatics” to recount prior interpretations of the field. Next, we articulate a holistic domain-specific informatics model to guide the study of cultural heritage within IS/LIS. We conclude by articulating ethical imperatives and new directions for Cultural Heritage Informatics in research, teaching, and practice. Informatique du patrimoine culturel: Passé, présent, et futur RésuméCet article retrace brièvement l’histoire informationnelle et la reconnaissance formelle récente de l’Informatique appliquée au Patrimoine Culturel en tant que domaine distinct au sein de l’IS/LIS, en réponse à des demandes croissantes pour une réflexion plus critique, éthique, et communautaire sur les pratiques de l’IS/LIS et l’identité du domaine. Nous analysons l’histoire conceptuelle et la géographie intellectuelle disciplinaire du patrimoine culturel et de l'informatique afin de retracer les précédentes interprétations du domaine. Ensuite, nous articulons un modèle informatique holistique, spécifique au domaine, pour guider l’étude du patrimoine culturel au sein de l’IS/LIS. Nous concluons en articulant les impératifs éthiques et les nouvelles directions de l’Informatique appliquée au Patrimoine Culturel en matière de recherche, d’enseignement, et de pratique.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.257 | 0.109 |
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".