Understanding Public Evaluation: Quantifying Experimenter Intervention
- Submitting institution
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University of Glasgow
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11-01056
- Type
- E - Conference contribution
- DOI
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10.1145/3025453.3025598
- Title of conference / published proceedings
- Proceedings of ACM SIGCHI 2017
- First page
- 3414
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- May
- Year of publication
- 2017
- URL
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http://eprints.gla.ac.uk/135020/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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1
- Research group(s)
-
-
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- ORIGINALITY: Automatically quantifies the observer effect for stewarded and unstewarded evaluations in public spaces for the first time using novel remote pedestrian tracking techniques. The results provide new quantitative insights into the ways observation distorts empirical data for “in the wild” studies. SIGNIFICANCE: This paper demonstrates a data-driven approach for urban planning and placement of technology, and lays foundations for a systematic framework for evaluating observer roles and evaluation methods in public spaces. RIGOUR: The work was given a prestigious Best Paper Award (chosen from top 1% of submissions) at ACM CHI 2017, the premier venue for research in HCI.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -