THE OZ BEHIND THE CURTAIN EFFECT: ETHICAL PERSPECTIVES ON THE ASSESSMENT OF SCIENTIFIC MERITS
Bibliographic record
Abstract
There is a genuine desire in Romanian research institutions to follow the rules of scientific evaluation tested over the decades in stronger academic and scientific communities such as those in the USA or the Western Europe countries. The main objective of our paper is to show that before enthusiastically embracing such rules of scientific merits evaluation a closer analysis of those norms is in order. Although the peer review system is the best method used so far, we have to analyse it and even criticize it in order to improve it. There are two points to be discussed in view of offering the right perspective on how the rules of scientific merits evaluation function: the peer-review system and the number of citations criterion. In the first part of our paper we shall investigate the shortcomings of the peer-review and the particular situations proving that the double blind review system does not always work to the benefit of scientific progress. In the second part of our study we shall examine the formalism undermining the number of citations criterion and show that we can find better alternatives. Those alternatives are not mere speculations: even prestigious institutions such as “Natural Science and Engineering Research Council of Canada” for example are giving up on the “classical” way of evaluating the scientific merits of researchers by shifting towards the content of the articles and not the number of their citations.
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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 | 0.177 | 0.281 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.119 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.023 | 0.026 |
| 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".