MS8 Choice of Vocabulary Publication platform for SSHOC
Bibliographic record
Abstract
It is important to understand the general framework of use for a vocabulary publication platform for the SSHOC project. In SSHOC, it is crucial to support better discovery of SSH research data in order to ensure better access and reusability. This will be made possible through support for multilinguality. In infrastructures, metadata aggregation platforms are provided that map metadata to a shared common ontology usually in English. A vocabulary server and publication platform will be provided in SSHOC to maximize the accessibility and to improve discovery of content by non-native speakers, thus allowing multilinguality. To achieve this, this Milestone reports on: (a) a survey of existing systems for managing (editing and publishing) and accessing (browsing) vocabularies used to describe and allow discovery of research data in SSH infrastructures; the systems are described based on relevant features: https://docs.google.com/spreadsheets/d/1MV2g1PZMQ_Rx8m8tybthOY9eMuFNpdJ xUCfvu-jxuLI/edit#gid=0 (b) a series of interviews with experts to identify the core features of vocabularies and vocabulary platforms and what was missing: https://docs.google.com/spreadsheets/d/1FSDlEPBfYdRU6TQgPXoWofpibkAGGv3Yz ZrMy0FBsIo/edit#gid=0 The two documents1 have been cross-compared to extract a list of relevant criteria and characteristics that the SSHOC publishing platform should have to support the editing, linking and publishing of the vocabularies. In addition, the following technical criteria have been identified and compared, such as the installation requirements, availability of an API and source code, implementation of Linked Data, and license type. The feature comparison table is available at: https://docs.google.com/spreadsheets/d/1s5_StggMB1AburKRG4U6kK6oj5Dsc4weaGtXw0w1YN A/edit#gid=02 The support documents (1. survey of existing systems – 2. series of interviews with experts – 3. set of criteria for comparison) are also provided in the Appendices to this report. Note that in version 1. 2 of this document, new information was added wrt. the current situation (Q1 2021) for the use of vocabulary platforms in the CLARIN infrastructure and the results of the CLARIN Vocabulary initiative actions, which were added as chapter 4 and appendices. New information in the existing chapters was added in Italics.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.129 | 0.113 |
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".