Bibliographic record
Abstract
When metadata becomes knowledge, opportunities for multiplicity and risks of harm and exclusion arise. As GLAM institutions contribute to the Semantic Web, we must pay attention to the implications of participation. While the Semantic Web grew out of the flourishing of web technologies in the 1990s, recognizing its roots in classical/symbolic AI (referred to as Good Old Fashioned Artificial Intelligence, or GOFAI)—in particular, expert systems and knowledge representation—encourages critical questions like: which problems from knowledge representation and expert systems does the Semantic Web inherit? Are GOFAI failures really failures, or does the gap between rhetoric and practice point to generative possibilities (some of which can now be seen in Semantic Web initiatives)? What can we learn from AI critics, feminist approaches, and the unmasking of encyclopedic neutrality? This research article will explore how critiques of AI expert systems and Cyc, an ongoing project to create a common sense knowledge base, might apply to Semantic Webefforts like Wikipedia, Wikidata, DBpedia, and Schema.org.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".