Bibliographic record
Abstract
Abstract: Existing research articulates difficulties masks cause in the interpretation of emotions (e.g., Carbon, 2020). The COVID-19 pandemic is an unprecedented time in which the impact of the pandemic on individuals' emotional processing is yet to be determined. Previous work in our lab has looked at interactions between audiovisual perception, emotion recognition, and memory without the use of masks; this work and existing research provide a baseline for my current project investigating the detection of facial emotions based on auditory cues during mask wearing. The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) is a verified tool to help analyze emotional reactions in individuals that was used in conjunction with the Facial Masks and Respirators Database (FMR-DB) which displays images of individuals with different types of masks. Participants heard sentences neutral in content (e.g., "dogs are sitting by the door") spoken in either a happy, sad, or neutral tone accompanied by masked or unmasked ambiguous faces. The purpose of the present study was to see how vocal expression of emotion can change the emotions detected on faces. We expect participants to interpret the ambiguous non-masked faces in a strong emotional manner when listening to the emotionally-charged audios. We also expect participants to have greater difficulty interpreting masked faces and rating them more neutral despite the emotion of the accompanied audio. The findings for this study are influential during COVID-19 as they may help mitigate communication complications as a result of the pandemic.
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 | 0.000 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".