HCI Across Borders and Sustainable Development Goals
Bibliographic record
Abstract
As HCI Across Borders aspires to celebrate its fifth year at CHI, and the CHI 2020 venue of Hawaii signifies a coming together of four continents, the goal of the 2020 symposium is to bring our focus to themes that unify and foster solidarity across borders. Thus we select the United Nations' Sustainable Development Goals as our object of study. Many communities within CHI focus on the constrained and ephemeral nature of resources, including the HCI for Development (HCI4D), Sustainable HCI (SHCI), and Crisis Informatics (CI) communities, among several others. We contend that it is time for these communities to come together in addressing issues of global relevance and impact, and for many more to care. Additionally, as the venue for CHI shifts to Asia in 2021, we aspire to prepare the conference and its participants to grapple with themes that might offer a different and novel perspective when engaged within the Global South.
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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 | 0.042 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.047 |
| Scholarly communication | 0.035 | 0.033 |
| Open science | 0.003 | 0.043 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".