Looking Abroad for Inspiration to Enhance the Scholarly Work We Do at Home
Bibliographic record
Abstract
Growing up in Saskatchewan (a province on the Canadian prairies), I was fascinated with tales of exploration and dreamt about sailing off in search of adventure and new experiences.My family didn't travel much (aside from an epic road trip through South Dakota) but I was an avid reader with an imagination greater than average.I remember when my grandmother was one of the first people in my city to 'get the internet' and I would spend hours in her basement (it was dial-up after all) navigating my way through online encyclopedias and articles in my quest to learn everything I could about the world and the way people lived outside of what I knew.I was 16 years old when I finally left North America on a 2-week exchange to Japan.I'm pretty sure my parents were hoping this would satisfy my itch to travel (at least for some time) but it just got stronger from there.Next came England, and then Australia.Now, 20 years later, I have been privileged to visit, live, and work in many countries across the globe.One of the things I have learned throughout my journeys is that each time I step out of my own routine and setting, I learn something valuable about the world unknown to me before.Like how we learn when we travel, visit a museum, or watch programming that depicts how the world works outside of our own settings, there are endless lessons we can also learn from international scholarship and peers within the academy.Such as the experience of learning a new language, cooking technique, or how to access public transit, international scholarship can open our minds to different perspectives and new ways of understanding the data we collect, analyze, and share.While it may be easy to disregard a study reported from a different country (or even state or province) because we immediately question its relevance, we may be overlooking important considerations and learnings that can help extend our scholarly conversations and improve applicability of our own work to others.One of my favorite things to do when traveling is to attend a local cooking class.Whether it is using a new ingredient to enhance the flavor of a usual Tuesday night dinner or learning a new method to put things together in a different way (e.g., onion and chili grinding in Ghana!), we usually end up with something interesting, tasty, and many times enhanced from our usual routines and recipes.The same can be said when designing and implementing new educational research and scholarly projects.Perhaps there is a new audience response system gaining traction in the U.K. that might be adaptable to our own contexts, or a new simulated patient training program out of Malaysia that accounts for social and cultural variables known to influence assessment that may be applicable to our own diverse populations.Identifying these 'new' tools and methods and incorporating them into our local work and research may help to elevate our scholarship, bring international interest to our work, and keep those Wednesday morning writing sessions as appealing as the revamped Tuesday dinner the night before!Some of the most memorable moments I've had abroad are the conversations with locals and other tourists alike.It is always fascinating to learn about different ways of life, compare where our routines align and depart, and understand how cultural or contextual nuances may shift how people live, work, and play.I find that these points also make for great conversation starters with my friends and family after returning home.In the same way, international scholarship can be used to extend the conversations within our writing by forming part of our theoretical or conceptual frameworks, identifying cultural or contextual factors that may need to be considered for future scholarly work, or through discussions of how our findings align (or differ) from the broader global research in our area of study.1,2 Being able to discuss applicability or alignment of findings from a study in Canada with one from Chile, for example, may broaden our perspectives and lead to greater relevance of our results.Alternatively, findings that do not align with studies outside of our own settings may spark interesting research questions around cultural or contextual differences that could be further explored.3 Just as we like to keep in touch with friends we've met abroad via social media or otherwise, it is quite possible
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.026 | 0.017 |
| Scholarly communication | 0.029 | 0.013 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.090 | 0.097 |
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".