Bibliographic record
Abstract
Structuralists seek to describe a semiotic system by identifying sets of interchangeable units ( paradigms ) and specifying how they can be combined in textual structures ( syntagms ). They often employ a ‘commutation test’ originally developed by Prague school linguists. It is used to identify what information theorists refer to as ‘differences that make a difference’. The primary analytical method employed by many structuralists involves the identification of underlying binary or polar semantic oppositions (e.g., us–them, public–private) in texts or cultural practices. For Jakobson, oppositions consist of an ‘unmarked’ and a ‘marked’ form. Unmarked terms reflect what we ‘take for granted’: our default assumptions. Oppositions are seen by structuralist theorists as part of what some refer to as the ‘deep structure’ of texts. Some of these linkages (such as masculine–feminine, mind–body) are regularly aligned in cultural practices and texts so that ‘vertical’ relationships develop (such as masculine–mind, feminine–body). Algirdas Greimas developed ‘the semiotic square’, an influential structuralist tool for the semantic mapping of conceptual frameworks that goes beyond the simple either/or of binary oppositions. Some structuralists have applied Saussure’s distinction between langue (a particular language system) and parole (discourse, or language in use) to other cultural forms.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.135 | 0.051 |
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".