Linguocognitive modeling of a heterotopia (an analysis of commemorative discourse)
Bibliographic record
Abstract
The article is devoted to the linguocognitive peculiarities of the heterotopia in commemorative discourse, i.e. discourse of remembrance, which has risen to prominence in the era of communicative impact. The term “heterotopia”, introduced into the humanities by M. Foucault, is understood hereinafter as a communicative space for passing on collective memory in commemorative discourse. The article aims to build a linguocognitive model of the “collective memory” heterotopia on which commemorative discourse is based. The research material is 800 small-format English-language online texts published by American, British and Canadian politicians from 2018 to 2024. The texts were extracted from the personal digital files of the author of the present paper. The empirical material was analyzed with general scientific methods (analysis, synthesis, description, etc.) and types of analyzing language phenomena (componential, conceptual, categorial analysis, linguostylistic, discourse analysis, etc.). The modeling was implemented with the cognitive-matrix analysis. Having analyzed the research material, the author comes up with a three-dimensional dynamic cognitive-matrix model of the “collective memory” heterotopia, nuclear-peripheral in structure. The nucleus is formed by the “otherness – sameness” conceptual opposition, while the periphery consists of two cognitive contexts – “Past/Future” and “Present”, mirroring each other, with a permeable boundary in-between. The matrix is set in motion with the mechanism of the cognitive illusion which predetermines a certain interpretation of reality through “biases” in its representation, or viewpoints. The author reveals five “biases”, which targetedly activate the components of the matrix and help to overcome the boundary between the time planes. Means of verbalizing the “collective memory” heterotopia get revealed, precedent names and paremias playing the key role among them.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".