Proceedings of the 2020 Conference on Human Information Interaction and Retrieval
Bibliographic record
Abstract
We would like to begin by acknowledging that the fifth ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR 2020) is taking place on the traditional and unceded territory of the Coast Salish Peoples, including the territories of the x?m??kw?y??m (Musqueam), Skwxwu?7mesh (Squamish), and S?l?i?lw?ta?/Selilwitulh (Tsleil-Waututh) Nations. These nations have lived here for thousands of years and continue to live here today. We are thrilled to have the CHIIR community gather in Vancouver to carry on the tradition of bringing information retrieval, information behaviour and human computer interaction into conversation. As a single-track conference that accepts a range of paper and presentation types, CHIIR is an outstanding venue for interdisciplinary innovation, creative problem solving and usercentred design focused on information seeking and retrieval. And CHIIR continues to grow, with a record number of submissions (154) this year and a truly international group of participants from more than 18 countries. Our thanks and appreciation go to all those who joined us in making CHIIR 2020 a success. First among these are the dedicated Program Chairs: Orland Hoeber, Irene Lopatovska and Ioannis Arapakis, who have done outstanding work leading and supporting the Track Chairs and Program Committee. We especially appreciate the work of the Track Chairs and the Senior Program Committee members and are delighted with the resulting program. This includes much anticipated keynote talks from two accomplished researchers and leaders: Professor Christine Borgman (UCLA) and Dr. Meredith Ringel Morris (Microsoft Research), who graciously accepted our invitations. In a world of ubiquitous access, our keynote speakers give us pause to consider what access means for different kinds of content and user communities. For local arrangements and conference logistics, we had the fortune to work with a great team: Treasurer Kathy Brennan; Webmaster and Student Volunteer Lead Sam Dodson; Publicity Chair Jocelyn McKay; Proceedings Chair Rick Kopak and Local Arrangements Team: Amelia Cole, Vanessa Figueiredo, Limor Tamim and Marina Botnaru. The ACM Conference Management Team and CHIIR Steering Committee also provided us with excellent support. Finally, we are grateful to SIGIR for sponsoring CHIIR and providing generous student travel awards, and to all our generous supporters who made it possible to put on this event.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.229 | 0.200 |
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".