Bibliographic record
Abstract
To the Editor: MacLeod and colleagues’ Research Report identified unintended “technologies of exposure” in their data in terms of the revelation of concealed facts (e.g., being magnified on a large screen, comments being heard unintentionally).1 Their article was very pertinent to the March 2020 Ottawa Conference on the Assessment of Competence in Medicine and the Healthcare Profession, Kuala Lumpur (KL), Malaysia, which had fewer people attending in person than had registered because of COVID-19. Response varied by institution and individual, but many people who had registered for the conference did not attend because of concerns about infection risk, infection control, and the potential sequalae of returning home and having to enter quarantine. Those absent from the conference often livestreamed their contribution or submitted a preprepared video. As an attendee, presenter, and session chair, I reflected on Macleod and colleagues’ comment that “Notions of privacy … are reshaped” through videoconferencing.1 One plenary speaker livestreamed from his study in Europe, where we could see his beautiful and well-stocked bookshelves. I could not help but wonder what the rest of his house looked like. Another colleague videoed in from his home study, exposing a drum kit behind him. I became curious about his family life—did he have kids? Did the drum kit belong to one of them? Or was it his? These insights into my colleagues’ personal lives are also present in everyday meetings. When I Skype, Zoom, and FaceTime colleagues around the world, I am familiar with their studies, kitchens, and family rooms—as they are with mine. We have had babies, and toddlers join calls unexpectedly and heard partners/spouses’ voices in the distance. I appreciate the affordances provided by these (digital) glimpses into my colleagues’ lives and the sense of connection they give. There is also a clear environmental gain from using technology for global connections rather than air travel. However, this intimate, even voyeuristic, glance into others’ domestic space communicates so much. Exposing our homes represents both a form of self-expression and a loss of anonymity. We must be aware of how videoconferencing might be eroding the boundary between home and work, and shaping the experience of absence/presence. Some may be happy with “uncurated exposure,” allowing those at the other end of the video call or in the conference room to see into their homes, enabling glimpses into their material surrounding. For those who do not want to open up their personal world in this way, sit in front of a blank wall or use the software to put yourself on standard issue background. Then, people like me can only muse about why you choose one background over another…. Videoconferencing may make our world smaller, but ignoring the unintended exposures caused by technology has the potential to oversimplify its impact on academic communications and connectivity. Jennifer Cleland, PhDDirector, Medical Education Research and Scholarship Unit, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore; [email protected]; ORCID: https://orcid.org/0000-0003-1433-9323.
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 | 0.011 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".