Meaningful Information, Meaningful Lives: Principles of a Semantic Information Science.
Bibliographic record
Abstract
In the 1990s, cultural theorists who speculated about the implications of the Internet for society, education, interpersonal interaction and academic research tended to base their thinking on the assumptions of semiotics, or, in its most radical form, deconstruction. There was an emphasis on hypertext and hypermedia. The driving forces of that initial decade of the Internet have left us with a Semiotic Information Science: the study, design and implementation of communicating processes and relations – in a word, links –among of information. In libraries and businesses, archives and museums, we catalog, index, manipulate, store and retrieve information. The paradigm shift to a Semantic Web and a Semantic Information Science offers the strong hope that we can move towards a science and society of qualitatively greater knowledge and intelligence. I advocate an expansion of the meaning of Semantic Web from a set of standard data formats for including semantic content in web pages to semantics understood as the branches of linguistics, computer science and psychology that deal with meaning. A Semantic Information Science will focus on the contexts that give meaning to words (as in linguistic lexical semantics), emphasize the ineffable and experiential qualities of nodes of (as in psychological semantics), and deepen the meanings and interpretations of programming expressions (as in my proposed extension of computer science semantics). Semantic software (see the SBSGRID platform) will provide natural language access to databases, return answers to associative questions, bring together the flexibility of search with the precision of query, and contextually fathom the user’s needs. The more meaningful information of the Semantic Web and a Semantic Information Science will help us to work, play, learn and care for our health differently(ibid.) and give us more meaningful lives.
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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.061 |
| Scholarly communication | 0.018 | 0.035 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".