Bibliographic record
Abstract
Abstract: This paper, based on the Socio-Discursive Interactionism theoretical epistemological framework, aims at showing the importance of the notion of prototypicity to analyse texts that circulate in society. Regarding theoretical concepts, we consider, firstly, that texts are global communicative units that always interact with the social practice where they are integrated; consequently their textual linguistic materialisation depends on the language activity in which they are situated. Secondly, texts are obviously linked to a textual genre which has unique and generic aspects constantly interacting with each other. By considering these aspects, we can see how the notion of family resemblance or family airs related to that of prototypicity can give us leads to define a textual analysis methodology that takes into account the complexity of the text as our object of analysis. In order to prove the importance of the prototypicity to analyse texts we have chosen two representative texts of two textual persuasive genres: one editorial and one political poster that were circulated in Portugal in March 2002, at the time of the elections for the Portuguese Prime Minister. Our study provides evidence that a text has singular characteristics, but it also has generic ones related to genre aspects.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".