A Call for Engaging Context in HCI/MIS Research with Examples from the Area of Technology Interruptions
Bibliographic record
Abstract
This paper contributes to the discussion on future directions of Human-Computer Interaction in Information Systems (HCI/MIS) research by explicating the role of task- and social context. We show that context has not been sufficiently engaged, and argue why it is important to pay more attention to it in theory and design of future HCI/MIS research. Drawing on examples from the core HCI area of technology interruptions, we formulate a set of general research questions and guidelines, which allow us to represent the context of multiple users in continuous collaboration with multiple tools while working on tasks that are intertwined within business processes. These guidelines will generate new insights for HCI/MIS research and allow us to develop research that captures the changing nature of the computing environment.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.014 | 0.037 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".