Proceedings of the 2015 ACM on Workshop on Multimodal Deception Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 2015 ACM Workshop on Multimodal Deception Detection, WMDD 2015. As deception behavior permeates on almost every human interaction, there is growing interest to understand and recognize the nature of deceptive behavior in multiple domains. The goal of this workshop is to provide the participants with a forum to foster the dissemination of ideas on computational and behavioral methodologies for deception detection. We are very excited about the success of the first edition of this workshop and the unique opportunity of gathering researchers from different fields to share their perspectives on deception detection. The call for papers attracted submissions from United States, Canada, and Europe, which resulted in five accepted papers that will be presented during the workshop. The program also includes three excellent invited speakers: we are grateful to Dr. Yejin Choi (University of Washington), Dr. Jeffrey Hancock (Cornell University), and Dr. Ioannis Pavlidis (University of Houston) for agreeing to speak at our workshop. We couldn't have hoped for a better slate of speakers and presentations!
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.050 |
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 it