Reports of the Workshops Held at the 2017 International AAAI Conference on Web and Social Media
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
The Workshop Program of the Association for the Advancement of Artificial Intelligence's International Conference on Web and Social Media (AAAI‐17) was held in Montréal, Québec, Canada, on Monday, May 15, 2017. There were eight workshops in the program: Digital Misinformation, Events Analytics Using Social Media Data, News and Public Opinion, Observational Studies through Social Media, Perceptual Biases and Social Media, Social Media and Demographic Research, Studying User Perceptions and Experiences with Algorithms, and The ICWSM Science Slam. Workshops were held on the first day of the conference. Workshop participants met and discussed issues with a selected focus — providing an informal setting for active exchange among researchers, developers, and users on topics of current interest. Organizers from two of the workshops chose to include papers in the AAAI Technical Reports series (Observational Studies through Social Media and News and Public Opinion). Their papers were included as a nonarchival part of the ICWSM proceedings. Organizers from four of the workshops (Digital Misinformation, News and Public Opinion, Perceptual Biases and Social Media, and Studying User Perceptions and Experiences with Algorithms) submitted reports, which are reproduced in this report. Brief summaries of the other four workshops have been reproduced from their website descriptions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it