Towards linked data: Some consequences for researchers in the social sciences and humanities
Bibliographic record
Abstract
ABSTRACT This paper addresses the introduction of Semantic Web and Linked Data technology in Social Science and Humanities research. On the basis of a sample of existing research projects, we examine the impact that the technology has on the research methodology. Three main points of impact were observed. The first is the epistemological foundations of the research, including a focus on individual entities in the research area (or “atomization”), the reification of research objects, and the favouring of analytical skills from researchers. Secondly, in the data analysis phase, research relies more heavily on technical skills during data discovery and processing, and the technology induces growing confusion between data and metadata. Thirdly, we show how the types of models that are built often imply a new encoding of already existing information.
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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 | 0.152 | 0.172 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.013 | 0.041 |
| Scholarly communication | 0.030 | 0.048 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".