The Interface of <em>Nous</em> and Computer in Inter-disciplinary Research, Communication and Education
Bibliographic record
Abstract
This study is a meta-cognitive discussion about whether non-English scientists know about the existence of computer tools – such as monolingual, bilingual and multilingual electronic dictionaries, CAT [Computer Assisted Translation] tools - and whether they know how to use them in order to communicate their inter-disciplinary research internationally. It also discusses what is at stake when concepts such as inter-scientificity (i.e. “bar”, with 17 different terms in Greek) and reverse inter-scientificity (i.e. “πρόγραμμα” [: program] with at least 6 different terms in English) emerge. Then the author of this study claims that only human mind/intelligence (nous) - with the aid artificial intelligence (computer –CAT tools) and through different mental/cognitive processes (noesis) can establish certain criteria in choosing appropriate terms and expressions, so that an inter-disciplinary research can be communicated properly and thus (international) scientific communication can be achieved effectively. Finally, the author of the present study proposes that Higher Education Institutions [HEIs] in North America (the USA and Canada) and Europe should get involved in educating and training both their large number of international students and staff administrative and academic), if a proper international inter-disciplinary communication is to be attained.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".