Bibliographic record
Abstract
The digital age is the age of smart systems. Smart living has already emerged as the conceptual hallmark of the digital future. We have we or will soon have smart homes, cities, and all sorts of smart interconnected objects. This paper deals, firstly, with the meaning of smart as related to the Greek concept of metis or cunning intelligence, the contexts of use being not only of human beings but also of gods, animals and artificial devices. The 19th century application of the concept referred to devices in general and in the 20th century to digital devices and systems in particular for which the leading sense is human intelligence. At present, it is not human but digital intelligence that leads the meaning of smart. Artificial smart systems receive their goals from the outside even if they can further develop such goals, giving the impression that they have conceived their goals on their own. They behave as if they were guided by a 'who' while in fact it is just a reified one, or a 'what'. The difference between who and what is the basis of ethical thinking in the age of smart systems.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".