INSIDE THE LAB/OUTSIDE THE BOX: INTERPRETING NONVERBAL MESSAGES IN THE TEACHING AND LEARNING ENVIRONMENT
Bibliographic record
Abstract
The Dr. Mary A. Lynch Communication Lab at Cape Breton University is the first lab of its kind and the longest running lab in North America. A mandatory requirement of our introductory Communication classes, the weekly experiential learning lab sessions help students understand concepts, increase their self-awareness and effectiveness as communicators, understand cross-cultural perspectives, retain the theory learned in class, and develop their communication skills cognitively, affectively, and behaviourally, through small-group discussions, experiential learning activities, and critically reflective written journals. This paper provides a history of the Dr. Mary A. Lynch Communication Lab, explains the purpose and methodology of the lab, and how our pedagogical approach demonstrates teaching outside the box. Nonverbal communication accounts for the majority of the messages we send. It’s also the primary way we construct and send messages about our identity unique to contexts and cultures. Educators and students are constantly sending and interpreting nonverbal cues. This paper explores the nine forms of nonverbal behaviours as presented as a critical reflection activity to participants at the Atlantic Universities’ Teaching Showcase and is representative of an activity delivered in the Communication Lab. Included are the questions posed to participants and a summary of the discussion surrounding each form of nonverbal messages in the context of the teaching and learning environment.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".