Adjusting to the Reign of Webinars: Viewpoint
Bibliographic record
Abstract
BACKGROUND: With the integration of COVID-19 into our lives, the way events are organized has changed. The Cerrahpaşa Neuroscience Days held on May 8-9, 2021, was one of the conferences that was affected. The annual conference of the student-based Cerrahpaşa Neuroscience Society transitioned to the internet for the first time and had the premise of going international. OBJECTIVE: With this study, we aim to both discuss how a virtual conference is organized and perceived, and where our conference stands within the literature as a completely student-organized event. METHODS: The conference was planned in accordance with virtual standards and promoted to primarily medical schools. During the execution, there were no major issues. The feedback was collected via a form developed with Google Forms. RESULTS: Out of 2195 registrations, 299 qualified to receive a certificate. The feedback forms revealed a general satisfaction; the overall quality of the event was rated an average of 4.6 out of 5, and the ratings of various Likert scale-based questions were statistically analyzed. Open-ended questions provided improvement suggestions for future events. CONCLUSIONS: The virtual Cerrahpaşa Neuroscience Days was a success in organization and received positive feedback from the participants. We aim to ground future events on this experience.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".