The value of presenting at scientific conferences: reflections by a couple of early career researchers
Bibliographic record
Abstract
Homo academicus , to use a term coined by Pierre Bourdieu, is a special species and for those of us embarking on our careers, we must be able to demonstrate our potential ability to not only survive but to excel in the academic jungle if we hope to secure our first post as an assistant professor or a junior researcher. The selection criteria include a lengthy list of publications in journals with the highest possible impact factor, an established research programme demonstrating the capacity to be an independent researcher, and extensive experience as communicators, both as invited speakers and as presenters at scientific conferences. Although there is much to be said on all of these aspects, our commentary focuses on this last point, as we should not forget the words of Max Weber:1 “of all of the pedagogical tasks, the most difficult is that of communicating scientific problems in such a way that an unprepared yet gifted individual will be able to understand and …
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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 | 0.072 | 0.230 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.030 |
| Scholarly communication | 0.031 | 0.022 |
| Open science | 0.012 | 0.016 |
| Research integrity | 0.047 | 0.096 |
| Insufficient payload (model declined to judge) | 0.002 | 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".