O tempo das Tecnologias Digitais da Informação e Comunicação e os sujeitos da educação de jovens e adultos
Bibliographic record
Abstract
The study of history is a reading of time, temporalities, and human habits. The way we organize ourselves in society and in time influences daily practices and epistemologically restructures the subject. In the speed time of Digital Information and Communication Technologies -DTIC, the subject is diluted in the context of the Information Society and of new technologies. Memory can be stored in digital media, memories stored in photos, music in pen drives, calculations in digital calculators, appointments in e-mails, friends in social networks, and work in computers, transporting social and cognitive human activities to the virtual environment, making these media constituents of the subjects and the contact with these DTIC the link of legitimacy among the members of this society. Digital language permeates all aspects of the subject, from the social to the cognitive, allowing him/her to appropriate the DTIC as a new epistemological technology of intelligence, just like oral and written language, which reorganize the way human beings think and their reading of time and space. Given the epistemological importance of the appropriation of digital language in contemporary society, its social emergence is implicit, serving as the ultimate and most intentional element of social exclusion in this society. In order for the individual to be part of the Information Society and to appropriate new technologies, he/she needs to incorporate the digital language through digital literacy, which from this perspective is as important as literacy in the written language for the development of the student. In face of this panorama, the students of Youth and AdultEducation end up being even more marginalized, since they are people from economically lower classes, who have not yet been literate in the written language, with older age, who did not have access to computers in childhood or currently and that in their formative process the ICTs are not taken into account as one of the main elements necessary for the literacy of these subjects, for their effective inclusion as members of this society based on the ICTs, so that they can appropriate the digital language and be included in society as citizens and autonomous individuals.
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".