MS14 Inventory of existing (meta-)data format interoperability solutions
Bibliographic record
Abstract
This document reports on the SSHOC project Milestone 14 inventory process and results. Earlier work of task 3.5 delivered D3.1 Report on SSHOC (meta)data interoperability problems, which provided an inventory of metadata and data formats used by the SSHOC communities, recommendations for metadata standards and data file formats, and priorities for providing conversion services. MS14 report provides an inventory of existing metadata and data format conversion solutions, technology and practices relevant to the social sciences and humanities per D3.1. These conversion solutions will provide the content for the SSHOC Interoperability Hub and are a starting point for better interoperability. Limitation of scope to (meta)data conversions is explained in D3.1. When investigating (meta)data conversion solutions, the team has identified two groups of solutions: A ready to use web-service that is provided by a research infrastructure, research center or organisation, or a commercial company; Complex solutions that can entail the use of several services and manual steps. In the remainder of this report, this type of solutions are referred to as conversion recipes that need to be well-documented. The sources used in the inventory process were D3.1, re-analysis of the expert interviews conducted in spring 2019, desk research to collect information about existing solutions, and different available SSH service registries and other information sources, e.g. TAPoR, CLAPOP, CLARIN VLO, PARTHENOS SSK, and DDI Tools.
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 | 0.085 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.032 | 0.025 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.027 |
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".