Assessing Information Reliability through Snapchat: An Alternative Means for Social Media
Bibliographic record
Abstract
This presentation was given at the 2018 ACRL North Dakota-Manitoba Annual Symposium at St.John's College in Winnipeg, Manitoba. The Albert D. Cohen Management Library at the University of Manitoba is currently piloting a new use for social media. Snapchat Reference is a focused effort to target students on a platform they are familiar with in order to expand the scope of the library’s reference services. Students are encouraged to ask questions about the sources they are using and engage in critical thinking and evaluation skills in a real-time conversation with a librarian. Different from other virtual reference services, Snapchat provides image exchange and messaging opportunities that afford the development of personal rapport with subject librarians. This presentation will highlight the creation and implementation of this service inregards to assessing reliable resources and furthering information literacy skills in post-secondary students.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.016 |
| 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; both teacher heads agree on what is shown here.
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".