How to Facilitate Cooperation between Humanities Researchers and Cultural Heritage Institutions. Guidelines
Bibliographic record
Abstract
The overall objective of this report is to support collaboration between humanities researchers (literary and cultural studies, history, arts) on the one hand, and cultural heritage institutions on the other, by raising awareness about the possibilities for reusing heritage resources in academic settings and increasing the visibility of online heritage collections. This publication aims to provide both cultural heritage institutions and researchers with know-how, examples of good practice which will enable and strengthen collaboration between both sides, and enable a greater circulation and reuse of heritage resources within the academic field. This document was prepared during a hands-on workshop for representatives of the European academic community and heritage professionals who are working to share their collections online in order to promote digital methods and the academic reuse of heritage content. We engaged humanities researchers who expressed an interest in exploring digitised cultural resources, and heritage professionals who create internal institutional policies for providing access and sharing resources online. The workshop took place at the Digital Humanities Centre at the Institute of Literary Research of the Polish Academy of Sciences in Warsaw (Poland) on 19–20 June 2018. Invited experts included Natalie Harrower (Digital Repository of Ireland), Mark Sweetnam (Trinity College Dublin), David Brown (Trinity College Dublin), and Marcin Werla (Poznań Supercomputing and Networking Center). Twelve participants from various European countries were recruited through an open call for contributors (they are listed as co-authors of this document). The workshop participants explored the main problems associated with heritage reuse in the context of their expertise and later translated those discussions into this document through a ‘book-sprint,’ which was facilitated by Kamil Śliwowski. The workshop and the preparation of the guidelines were funded by a DARIAH Theme 2017 grant, which was awarded for the project ‘Facilitating Cooperation Between Humanities Researchers and Cultural Heritage Institutions,’ jointly proposed by the Institute of Literary Research of the Polish Academy of Sciences, Trinity College Dublin, and Creative Commons Polska.
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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 | 0.134 | 0.210 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.034 | 0.015 |
| Insufficient payload (model declined to judge) | 0.034 | 0.035 |
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".