SSHOC'n Tell Challenge - How to use VCR and the Switchboard in teaching and training
Bibliographic record
Abstract
What? Our idea is about applying research tools like the Switchboard, the Virtual Language Observatory (VLO) and the VCR - for teaching scenarios Why? We see two main benefits in this idea/challenge, one addressing the students side, the other addressing the tool/services side. Regarding the students: Learn with and use these tools the students enhance their data discovery and processing skills and also learn how to cite the datasets they use. They gain important contributions to their skillset regarding Research Data Management (RDM), collaborative modes of working (e.g. in the European Research Area) but also addressing the FAIR principles as a per-default mode of doing research today. Regarding the tool side: a common weak spot of research infrastructures is the missing connection between the developer/provider side and the user community. Often this leads to an unsatisfactory uptake in the community and vice versa to deficits regarding user experience or unsatisfactory integration in the research processes. The intensive use in a teaching environment could address this weak spot. Additionally this idea/challenge addresses an improved integration and interoperability of the mentioned tools and resources with one another.
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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.072 | 0.049 |
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".