ComSciConCAN and the Positive Impacts on STEM Graduate Students’ Confidence and Sense of Belonging
Bibliographic record
Abstract
Background: Although Science Communication (SciComm) is a growing field, there currently exist a large number of undergraduate- and graduate- level SciComm training programs worldwide. Two noted benefits of such programs are improvement in SciComm confidence and sense of belonging. ComSciConCAN is a graduate level SciComm conference; no studies have previously looked into the impacts of this conference, which encouraged us to initiate this study. Purpose: To explore the impacts of ComSciConCAN on the participants’ confidence in communicating with other scientists and the general public, and their sense of belonging in their current STEM programs and SciComm activities Methods: ComSciConCAN-2021 participants were asked to complete pre- and post- workshop surveys. Data analysis was done using Microsoft Excel, and unpaired t-test statistical analysis was conducted (when applicable) using Graphpad. Results: With regards to confidence, significant differences (p < 0.05) were observed in the mean levels reported for all three cases pre- versus post- conference. Regarding sense of belonging, 65% and 83% of the participants reported at least “somewhat agreeing” that the workshops will help improve this in their current STEM program and SciComm activities, respectively. Conclusion: The conference did have a positive impact on the participants’ SciComm confidence and senses of belonging.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 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".