Third ASIS&T student design competition: The “truthiness” challenge
Bibliographic record
Abstract
Abstract Editor's Summary For the third year, students attending the ASIS&T Annual Meeting were invited to compete in a design challenge, this time focusing on “truthiness.” The task was to devise a user opinion site that would distinguish fake reviews from valid and trustworthy ones. Students settled into four teams, based partly on the similarity of their shoes, and chose topics for their sites. Over two days, grabbing time between conference sessions, the teams gathered to plot out aspects of their site including system architecture, input and output features and user interaction. Teams then described the hypothetical sites to a panel of four judges, who considered creativity, impact on the problem, feasibility, contribution to humanity and quality of the presentation. As members of the winning team of Pratt + S. Carolina + Mizzou, Matt Miller, Jeff Mixer, Ben Richardson, Dinara Saparova, Andy Steinitz and Yao Zhang will receive free registration for the 2013 ASIS&T Annual Meeting in Montreal.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".