Social semiotics as theory and practice in library and information science
Bibliographic record
Abstract
Purpose – Information scholars frequently make use of “conceptual imports” – epistemological and methodological models developed in other disciplines – when conducting their own research. The purpose of this paper is to make the case that social semiotics is a worthy candidate to add to the information sciences toolkit. Design/methodology/approach – Both traditional and social semiotics are described in detail, with key texts cited. To demonstrate the benefits social semiotic methods may bring to the information sciences, the digital display screen is then employed as a test case. Findings – By treating the display as a semiotic resource, the author is able to demonstrate that, rather than being a transparent window by which the author may access all of the data, the screen actually distorts and conceals a significant amount of information, and severely restricts the control users have over software packages such as online public access catalogues. A programming paradigm known as language-oriented programming (LOP), however, can help to remedy these issues. Originality/value – The test case is meant to provide a framework by which other information sciences issues may be explores via social semiotic methods. Social semiotics, moreover, is still evolving as a subject matter, so IS scholars could also potentially contribute to its continued development with their work.
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 | 0.020 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.089 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".