Bibliographic record
Abstract
As a journal representing the international community, Epilepsia has a mission to support and disseminate high quality research from all regions of the world, and the Journal strives to maintain transnational balance and perspective. However, it is not uncommon to receive oral comments and letters, like the one by Hu et al. in this issue, that question if Epilepsia really serves the international community or if the Journal favors one country over another with respect to published manuscripts. Such a question is not unreasonable, and as Editors it is our view that the best way to address this question is through data. Table 1 shows the number of papers submitted to and accepted by Epilepsia from 2008 to 2013 according to country. Since 2008, Epilepsia has received manuscripts from 88 countries from all regions of the world. Of these, 77 were countries with International League Against Epilepsy (ILAE) chapters, and 11 were countries without chapters. This represents 69% of all countries with ILAE chapters. There is a somewhat skewed distribution of submissions by country but not for number of papers accepted and acceptance rates. The mean (± standard deviation [SD]) number of submissions by country was 60 ± 160 (range 1–1,321), with a median of 6. Fourteen countries (16%) submitted >100 manuscripts each, which represents 79% of all submissions. These countries include Australia, Brazil, Canada, China, France, Germany, India, Italy, Japan, Korea, The Netherlands, Turkey, United Kingdom, and the United States, with the United States being the highest (25% of all submissions). The mean (±SD) number of accepted papers by country was 20 ± 63 (range 0–532), with a median of 1. Twenty-nine countries had 10 or more papers accepted by Epilepsia from 2008 to 2013, which was 96% of all published papers for that period. Those 29 countries represented all ILAE regions except for Africa, with the highest number from the United States (the same country with the highest number of submissions representing 30% of all accepted papers). The mean (±SD) percentage of accepted paper by country was 25 ± 28% (range 0–100%), with a median of 19%. Of countries that submitted 20 or more papers from 2008 to 2013, 22 (25%) had acceptance rates of 30% or greater. The five countries with the highest acceptance rates were Denmark (62%), New Zealand (58%), Australia (51%), United Kingdom (46%), and Hong Kong (44%). What do these data mean with respect to fulfilling the mission of Epilepsia as an international journal and having a paper accepted by individual country? Epilepsia is fulfilling its mission by publishing high quality papers from around the world. Many published papers are from the United States; however, this is also the country with the highest number of submissions, so it should not be a surprise. Other countries also have higher acceptance rates. Hence, Epilepsia does not appear to discriminate in accepting papers by country. As is true for journals with higher impact factors, Epilepsia makes acceptance decisions according to the quality and impact of submissions. As presented by Phil Schwartzkroin, immediate past Editor-in-Chief of Epilepsia in the accompanying letter from Dr. Hu and colleagues, those qualities include submissions that focus on important and interesting topics; have clearly stated goals and objectives; the observations and findings are novel and change the way we think about epilepsy; and the study is well designed and includes adequate sample size, comparison groups, and statistical analyses. It is also important that the data are appropriately interpreted and not overly construed. We hope this addresses the question about publication by country for Epilepsia. The Journal is committed to publishing the best quality and high impact research related to epilepsy, especially of international importance. As Editors, we encourage authors, especially new investigators, to consider submitting your work to Epilepsia. We promise to give your work a fair review without bias to geography, but with a bias toward quality and impact. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines. Neither of the authors has any conflict of interest to disclose.
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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.030 | 0.028 |
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".