Data School Diary - Day 09 - Data Steward Panel
Bibliographic record
Abstract
Audio diary: Data Steward Mini-Panel of CODATA-RDA Research Data Science School 2019 held at the Abdus Salam International Centre for Theoretical Physics in Grignano, near Trieste in Italy between August 5 and August 16. Discussants include members of the data steward stream: Frans Huigen, Data Archiving and Networked Services (DANS) Sanjin Muftic, Digital Scholarship Specialist, University of Cape Town Libraries Cristiana Pisoni, Repository Manager, Universita Degli Studi Di Bergamo Sothearath Seang, Policy Officer and future Open Science Ambassador, Eurodoc Organized by Andjani Gatzweiler. TOC:Introductions 0:00.000 What is a Data Steward? 2:35.479 What do you take away? 7:26.440 Impressions and Suggestions 13:16.781 Most Memorable 17:24.837 Refers to events of 2019-08-15. Recorded with Tascam DR-05 on 2019-08-15 at Adriatico Guest House. Processed with Adobe Audition.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.548 | 0.438 |
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".