On the topicality and research impact of special issues
Bibliographic record
Abstract
The publication of special issues constitute an important yet underinvestigated phenomenon of scholarly communication. In an attempt to draw attention to the proliferation of special issues, Priem (2006) suggested that their commissioning has an underestimated opportunity cost, given the relative scarcity of publication space: by distorting the “marketplace for ideas” through the commanding of preselected topical distributions, special issues undermine the total research output by “squeezing out” high-quality but topically unrelated articles. The present paper attempts to test this hypothesis by providing a topicality and research impact analysis of conference-based, monographic, and regular issues published between 2010 and 2015 inclusive and indexed in Clarivate Analytics’ Web of Science. The results show that the titles and abstracts of articles copublished are topically closer to each other than those copublished in regular issues, which suggests that their relative importance might influence the total topical distribution. However, disciplinary and overall comparison of relative citations for both special and regular issues shows that intraissue averages and variances in the former case are respectively higher and lower than in the regular issue context, which undermines not only the abovementioned hypothesis, but also the belief that editors often “fill up” special issues by accepting substandard papers.
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 | 0.025 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.033 | 0.030 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".