Cite Your Well-being First: What Happens When Personal Life, Mental Health, and HCI Research Become Entangled?
Bibliographic record
Abstract
Human-Computer Interaction (HCI) research often requires deep engagement with people and their environments, making the researcher’s own well-being an integral, yet overlooked factor in the research process. Personal challenges, ranging from academic pressures to difficult life events, can influence how we conduct studies, interpret data, and relate to our work. Despite this, such experiences are rarely acknowledged in formal academic spaces, and there is limited discussion about their impact on research. Our workshop offers a space for HCI researchers to reflect on their well-being, share personal experiences, and examine how personal struggles intersect with their research practices. Together, we will foreground researchers’ well-being as an essential concern and explore how these lived realities can be meaningfully integrated into our methodologies. In doing so, we invite the HCI community to not only centre the human in our research, but also recognise the researcher as human; one whose life is deeply entangled with the work they do.
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 | 0.036 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.021 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".