Slides From "E-Humanities And E-Heritage Research Infrastructures: Beyond Tools" (Parthenos Ehumanities And Eheritage Webinar, Thursday, 22.02.2018, 11:00 – 12:00 A.M. Cet)
Bibliographic record
Abstract
This webinar will provide a theoretical basis for a general understanding of the digital and infrastructural turn in the (Digital) Humanities and Cultural Heritage along with theoretical and critical reflections around the topic: Which opportunities and challenges do Cultural Heritage and Digital Humanities Research Infrastructures offer for research(ers)? The webinar will further explain how different stakeholders can engage with Research Infrastructures (community engagement and user engagement). Examples are research communities and networks, transnational digital collections, thematic working groups, overarching organisations (ERIC platforms, the Research Data Alliance), conferences and workshops, Transnational Access, Research Infrastructures in Academic Curricula. As a general introductory webinar it will also touch on some aspects of the other webinars in the PARTHENOS Webinar Series including a short introduction of the research lifecycle. This webinar will feature “teasers” from several Research Infrastructures, such as short introduction videos or webcontent). Read the full description of this webinar and gain access to more resources (e.g. the webinar recording).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.655 | 0.383 |
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".