Engaging Graduate Students with an Images of Research Competition
Bibliographic record
Abstract
Abstract Background In the fall of 2014, librarians secured a grant from the Teaching and Learning Enhancement Fund to implement programming in support of graduate student professional development and community building. As part of this initiative, the Images of Research competition was launched in partnership with the Faculty of Graduate Studies and Research, and has grown into a much-anticipated annual campus event. Purpose This competition leverages library expertise and spaces to promote the varied types of research occuring on campus. In addition to providing graduate students with the opportunity to tell the story of their research to the broader campus community, the Images of Research competition allows them to improve skills in succinct communication while fostering community and enhancing student experience. Method Once a year, graduate students are invited to submit a high-resolution image plus a short, plain-language description explaining how the image relates to their research. All images that meet the technical requirements are adjudicated by a judging panel consisting of faculty, graduate students, and (when possible) a communications professional. Entries are judged on their originality, aesthetic appeal, relationship between the image and the student’s research and clarity of the accompanying description. The top 24 images are selected as semi-finalists and subjected to online voting to determine a People’s Choice award winner. Competition organizers use a variety of online tools to facilitate competition submission, judging and voting. People’s Choice voting has in some years been restricted to our university community and in others left open to the wider world; graduate students have indicated a preference for university-only restrictions. All competitors grant permission to include their entry in our Open Access institutional repository, and the winners and semi-finalists are uploaded after the conclusion of the competition. Results Prize winners are announced at a catered reception in the library, and an exhibition of winning and semi-finalist entries remains on display for approximately one month. Prize winners are not eligible to enter future competitions, but other entrants, including semi-finalists, are welcome to re-submit with a new image. Organizers view the presence of repeat competitors over the years as a measure of the competition’s success. Entrants are surveyed after the competition so that their feedback can guide future iterations of the event, and the overall response has been extremely positive. Conclusion The resulting images have been featured in our alumni magazine, on social media, as displays for meetings and in the Faculty of Graduate Studies and Research offices. The involvement of campus partners is a significant contributor to the ongoing success of the competition.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.013 |
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".