Bibliographic record
Abstract
In the last thirty years we have moved from a substantially analogue communication, and education, to an almost completely digital world. We have moved from separate worlds, and competences, to an integrated and interconnected world. The barriers of knowledge and expertise have been broken down, and with them also the principle of mediation and authority in information and knowledge. To fully fulfil our role as educators, we need to make a further effort: to cross the boundary between disciplines and between knowledge. What is needed is a renewed encounter that allows us to merge and blend the new discoveries of neuroscience with the unique and unrepeatable experience of teachers. In a world that appears massified and standardised, we must return to the individuality of the person and grasp that valid element, that useful suggestion, for a model of democratic education that can truly contribute to leaving no one behind and 'no one excluded'. The sea of over-exposure and over-information in which we are all exposed and overexposed would like it to be an opportunity for a free and conscious encounter and confrontation with the other, with what is different from oneself, but in that same sea it is extremely easy and most likely to get lost. This article aims to hint at the recent discoveries of neuroscience regarding emotions, rest, exposure to social media and texting as a prevalent form of communication, regarding reading between paper and screen, for an enhancement of human subjectivity.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.031 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".