COGNITIVE-MATRIX ANALYSIS AS A TOOL FOR EXPLORING CULTURAL IDENTITY OF A FICTION AUTHOR: A STUDY OF M. PORTER’S “A GRANDMOTHER BEGINS THE STORY”
Bibliographic record
Abstract
The present paper belongs to a series of articles with a focus on ethnospecific concepts and identity construction in multicultural fiction from a cognitive-functional perspective. It seeks to take a new look on ethnospecific concepts, namely those which belong to an author’s idioconceptual subsystem, the latter being understood as a unique configuration of concepts in one’s mind, and constitute cognitive matrices. In accordance with N.N. Boldyrev’s research, the term «cognitive matrix» is used to denote a system of interconnected cognitive contexts which open onto different conceptual domains. The aim of the study is to demonstrate the potential of using cognitive-matrix analysis to reveal the connections between the cognitive contexts in the structure of an ethnospecific concept which unfold in a work of fiction, as well as the conceptual domains they are related to, on the one hand, and the many facets of an author’s cultural identity on the other. The paper outlines the results of the cognitive-matrix analysis of the concept BISON as represented in the novel “A Grandmother Begins the Story” by M. Porter, a Canadian writer of Métis background. Detail is given on several cognitive contexts in the structure of the concept in question pertaining to the following conceptual domains: NATURE, HISTORY, SPIRITUAL CULTURE, SPACE, TIME. The results obtained show that the ways in which the ethnospecific concept BISON functions in the text under consideration are affiliated with the writer’s identity and allow her not only to implement her creative ideas but also actualize her cultural identity in fiction.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".