Encouraging Scientific Collaborations with ConfFlow 2021
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We often find other collaborators by chance at a conference or by looking for them specifically through their papers. However, sometimes hidden potential social connections might exist between different researchers that cannot be immediately observed because the keywords we use might not always represent the entire space of similar research interests. As a community, Multimedia (MM) is so diverse that it is easy for community members to miss out on very useful expertise and potentially fruitful collaborations. There is a lot of latent knowledge and potential synergies that could exist if we were to offer conference attendees an alternative perspective on their similarities to other attendees. ConfFlow is an online application that offers an alternative perspective on finding new research connections. It is designed to help researchers find others at conferences with complementary research interests for collaboration. With ConfFlow we take a data-driven approach by using something similar to the Toronto Paper Matching System (TPMS), used to identify suitable reviewers for papers, to construct a similarity embedding space for researchers to find other researchers.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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 it