Bibliographic record
Abstract
Dr. Kori Inkpen is the CHCCS Achievement Award winner for 2017. For the past 25 years, she has worked in the field of Human-Computer Interaction (HCI), including ten years as a faculty member, first at Simon Fraser University and then at Dalhousie University, followed by another ten years in industry at Microsoft Research. Her research has focused on supporting collaboration in a variety of domains.For the invited publication by the award winner that CHCCS includes in the proceedings, again this year we are experimenting with an interview format rather than a formal paper. This permits a casual discussion of the research area(s), insights, and contributions of the award winner. What follows is an edited transcript of a conversation between Kori Inkpen and Kellogg Booth that took place on April 13, 2017, via Skype.
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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.008 | 0.023 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".