Phenomenon and manifestation of the `Author's Effect of Showcasing' (AES): a literature science study, II. Very heterogeneous documentedness of historically synchronous conference communications of a single natural science
Bibliographic record
Abstract
The `Author's Effect of Showcasing' (AES) is the activity of publishing authors who shape by their own free will the formal reference stock of their communications, placing this stock into the showcase of science. This paper reports the results of a decisive control test of the existence of the AES, processing 1175 historically synchronous physics conference communications. Applying methods of bibliometrics and science philology, the manifestation of the AES phenomenon is demonstrated and analysed in this theoretically most homogeneous domain of scientific literature. The widely differing documentedness in the communications of conferences held on particular topics of physics, especially the great differences in the size of the formal reference stocks in all extent categories of the communications depends solely on the person of the authors. This generally extremely heterogeneous documentedness is therefore valid evidence of the existence of the effect and its effective operation in the scientific literature. The correctness of the AES doctrine, including the correctness of two additional theses, has been demonstrated.
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 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".