Bibliographic record
Abstract
Today, the sharing of semantics remains a conundrum. Semantics can be shared within a universe of discourse, but individuals and communities cannot be relieved of the need to define their own universes of discourse. The emergence of collective intelligence is increasingly seen as necessary for human survival, but it is difficult for people who live in diverse universes of discourse to know when they are talking about the same things. Collective intelligence — the ability of a community to exhibit self-sustaining, rational behaviors — is inversely related to its participants' ability to understand each other. Diverse minds can create, recognize, and think in terms of diverse sets of distinct concepts and relationships between them. A conceptual addressing system can map such sets into a shared abstract "semantic space" that is structured by an algebraically definable group of transformations. Information Economy Meta Language (IEML) is such a "semantic space addressing system"; it defines a very large space of semantic addresses. A small number of the points in that space — more than 2,500 of them — are now listed in an "IEML Dictionary", along with interpretations of each of them in several natural languages. A language for compactly specifying sets of locations in the space exists, and a parser that translates expressions in this language into XML is available. A programming language for discovering and asserting relationships between sets of semantics is being developed, along with a variety of related software tools. The semantic space research program could provide a scientific (measurable, principled, experimentally repeatable) foundation on which technologies and professional disciplines can be created, including distributed collaborative semantic search engines, models and simulations of collective intelligences, tools and editorial practices for the automated production of multimedia documents, and many more.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.026 | 0.080 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".