Opening the Semantic Space in the Service of Collective Intelligence - DOI: 10.3395/reciis.v1i1.43en
Bibliographic record
Abstract
As the human recorded memory is progressively digitized and posted on line, the need for a common semantic coordinate system independant from natural languages and ontologies is growing. A future universal semantic addressing system, able to index all digital documents, should meet three basic requirements. First, each distinct concept should have a unique address. Second, the semantic coordinate system should be open to any concept and relations between concepts (ontologies), whatever the cultural environments where these concepts are created and transformed, without neither privileges nor exclusions. Third, it should support a group of mathematically defined (automatable) operations on semantic addresses, namely : rotations, symmetries and translations in the « semantic space » ; semantic compression and decompression ; set-theory operations like union, intersection and symmetric differences ; ranking on semantic criteria ; semantic pattern recognition ; semantic distances measurement ; logical inferences, etc. Developped by an international research network led by the Canada Research Chair in Collective Intelligence at the University of Ottawa, the Information Economy MetaLanguage (IEML), allows the construction of a semantic coordinate system meeting these three constraints. Website, including the IEML dictionary, since may 2006 : www.ieml.org. In Brasil, BIREME (www.bireme.br) is member of the IEML initiative.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.164 | 0.107 |
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".