Phenomenon and manifestation of the `Author's Effect of Showcasing' (AES): a literature science study, I. Emergence, causes and traces of the phenomenon in the literature, perception and notion of the effect
Bibliographic record
Abstract
The `Author's Effect of Showcasing' (AES) is the activity of publishing authors who shape by free will the formal reference stock of their communications cited directly and item by item, placing this formal reference stock into the showcase of science — consciously or unconsciously. This first paper of the study demonstrates the emergence, causes and traces of the AES phenomenon in the journal literature of the natural sciences already in the mature Little Science age, and the continuous existence of the phenomenon ever since. The perception and cognition of the effect is shown on the basis of the relevant findings of the present author's previous, manual fact-finding reference investigations based on autopsy, processing around 27,600 journal communications and reference stocks containing more than 322,000 citations. Finally, a summarizing definition of the notion of the effect is given. In a second paper, the manifestation of the AES phenomenon will be demonstrated and analysed in the theoretically most homogeneous domain of the scientific literature.
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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".