Invitation to solve puzzles: a proposal for the construction of literary literacy in the reading of poems
Bibliographic record
Abstract
This work aims to investigate and analyze the contributions of the Receptive Method and the expanded sequence of Literary Literacy, allied to the use of TDICs in order to reflect on the pertinence of the use of these two methodologies and the resources of the digital social networks in the development of the skills needed for the training of poetry readers in High School. The methodology used to organize the study was the qualitative approach of the ethnographic type. The study was carried out during the 2017 school year (in the form of a pilot project) and reapplied in 2018 at a school in the Campo Largo-PR state school system during a quarter term in each of the application situations, during the classes of Portuguese Language and Literature for groups of the 4th year of the Teacher Training Course for Early Childhood Education and for the initial years of Elementary School and had 36 participants in the pilot stage and 34 students in the second stage. The study describes and analyzes the work done using the 5 steps of the Receptive Method, organized by Aguiar and Bordini (1993), and the expanded sequence of the Literary Letters suggested by Cosson (2009), in reading poems of the work Claro Enigma (1951), by Carlos Drummond de Andrade. The data were obtained during the classes of the teacher-researcher, with photographic records, questionnaires, interviews and analysis of the students' productions. The results obtained with the triangulation of the data allowed to reflect on the limits and possibilities of the use of a combination of the steps of the Receptive Method with the expanded sequence of the Literary Literature for the formation of the poetry literary reader, besides demonstrating the pertinence and the viability of the use of Internet social networks in literature classrooms.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".