Past history and future challenges of \nHuman Work Interaction Design (HWID): \ngenerating cross-domain knowledge \nabout connecting work analysis and \ninteraction design
Bibliographic record
Abstract
Publications arising from these TC13 events are published as conference proceedings such as the INTERACT proceedings or as collections of selected and edited papers from working conferences and workshops.See http://www.ifip.org/for aims and scopes of TC13 and its associated Working Groups. 2. Working Group 13.6 on Human-Work Interaction DesignThis working group was established in September 2005 as the sixth Working Group under the TC13 on Human -Computer Interaction.It focuses on Human-Work Interaction Design (HWID) and it is called WG13.6.A main objective of the Working Group is the analysis of and design for a variety of complex work and life contexts found in different business and application domains.For this purpose it is important to establish relationships between extensive empirical work-domain studies and HCI design.The scope of the Working Group is to provide the basis for an improved cross-disciplinary co-operation and mutual inspiration among researchers from the many disciplines that by nature are involved in a deep analysis of a work domain.Complexity is hence a key notion in the activities of this working group, but it is not a priori defined or limited to any particular domains.The aim of this Working Group on Human-Work Interaction Design (HWID) is to initiate new research initiatives and developments, as well as an increased awareness of HWID in existing and future HCI educations.See http://hwid.cbs.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".