Bibliographic record
Abstract
The problem scientific misconduct, and of the evaluation of the scientific work is mentioned and some solutions are proposed. In the case of the recent scandal over plagiarism of some doctorates in Europe and Serbia, these concepts are presented here and criticized and other, more correct concept, are presented where it is insisted on the strategy of social piece meal engineering of Karl Popper. The evaluation of scientific text is often linked to some theoretical reflection about truth, beliefs, and meanings of the empirical evidence and the way of presenting and evaluating their cognitive values. There are, it is argued, such contexts, which, most importantly, are mutually exclusive and incompatible. In addition in such context, it appears that truth and falsehood are losing their independent meaning. Some global context can be accepted or rejected and therefore declared to be true or false, but certain entities that are inherent to these contexts are taken to be epistemicaly irrelevant and are over determined by the normative definitions of the context to which they belong irrespective of their isolated meanings and character. One of the most important applications of this approach is through individual entities, i. e, it is maintained that their cognitive value can be viewed to great extent in isolation from the context.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.064 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".