Bibliographic record
Abstract
Academic publications are too often ignored by other researchers. There are various reasons: Researchers know that conclusions may eventually be proved wrong; publications are sometimes retracted; effects may decline when studied later; researchers occasionally don’t seem to know about papers they have allegedly authored; there are even accusations of fraud (Cohen, 2011). In this exploratory case study, 10 papers were examined to determine the various ways they were used by others, whether there were cases of reported effects declining, and whether, among those who referenced the papers, there were suggestions that anything in the papers ought to be retracted. Findings showed that all the papers had been referenced by others (337 user publications were found, containing a total of 868 references). Other findings include the following: Single references were far more common than multiple references; applications/replications were the least common type of usage (23 occurrences), followed by contrasts/elaborations (34), and quotations (65); unlike reports regarding publications in the sciences, whether the paper was solo- or co-authored did not affect usage; appearance in a non-prestige journal was actually associated with more usage of some kinds; and well over 80% of uses were in heavily scrutinized sources (journal articles or theses/dissertations). The paper concludes with recommendations to writers about how to avoid producing publications that are ignored.
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 | 0.066 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".