Bibliographic record
Abstract
Since the new Editorial team assumed the reins here at the BJUI in January 2013, we have worked hard to embrace social media as the transformative communication technology it clearly is. While our priority is to only publish papers of the highest quality, we have also ensured that the reach and engagement of these papers are maximised using our social media platforms – Twitter, Facebook, YouTube, Instagram and [email protected] In this month's BJUI, there are two intriguing papers that deal with this area but that deliver contrasting messages. The first from Nason et al. 1 analyses the Twitter activity of all urology journals over a recent 6-month period. It is clear that the major journals have adopted Twitter as a preferred social media platform and it is gratifying to see how well the BJUI performs when assessed using the metrics in this paper. However, Fuoco and Leveridge 2 report that most urologists in a Canadian survey believe that ‘social media integration into medical practice is impossible’ and attitudes towards the professional role of social media were ‘generally negative’. Nevertheless, the power of social media in enhancing our personal and professional communication is undeniable and we at the BJUI expect more and more urologists to embrace social media in the coming years. We will continue to evolve our social media strategy to ensure the BJUI is a highly ‘social’ experience. On another note, the BJUI is pleased to support an excellent conference taking place in Dublin in April in the memory of our previous Editor-in-Chief, Professor John Fitzpatrick, who passed away suddenly last year. The Inaugural John Fitzpatrick Irish Prostate Cancer Conference takes place from 23–24th April 2015 and has attracted an outstanding International Faculty of colleagues and friends who look forward to exploring the most challenging areas in prostate cancer in his memory. Details here http://www.globalteamwork.ie/prostate.html. None declared.
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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.247 | 0.132 |
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".