Crowd Shape as a Visual Feedback Mechanism in Human-Computer Interaction
Bibliographic record
Abstract
In this thesis, we explore visual interfaces for crowd-computer interactions using a crowd shape generated from participating crowd members, in three main forms: precisea visualization of each tracked individual, blobby -an approximate outline or shape of all participants, and combined -both the precise shape and the blobby shape layered on top of one another.We wish to determine which of the three crowd shapes is most usable, while also helping determine which kinds of interactions and visual feedback is best suited to crowd-computer interactive installations.We performed usability studies asking participants to rate the efficiency, pleasantness, ease-of-use, and suitability, as well as answer an open-ended question about their experience of each crowd shape, for three sample applications.We hypothesized that the combination shape would be the most preferred, with the blobby shape being the next preferred, and the precise shape the least preferred.We developed 3 applications that tested 3 small crowds of 6-5-6 participants (predominantly 3 rd -4 th year university students) for the use of crowd-shape input and visual feedback in collaborative crowd exercises; and 20 (-2 removed) individual participants (predominantly 4 th year university students and ages 25-34 university graduates) that tested the understanding of crowd-shape from outside the crowd itself.We have found that the blobby shape is the least preferred, and the combined shape likely to be the most preferred.Considering the noise present in the data, we cannot conclusively verify our hypotheses, but the results provide strong indications to the value of both individual and group visual feedback when working with crowds.i And special thanks to my sister-in-law Dr. Angela Loder for her invaluable and concise feedback in this thesis' final drafts, and for her unfaltering encouragement throughout the entire process of this degree.v 1.On a scale of 1 to 7, how effective was the
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".