The medium is still the message: Canadian federal politicians' gestural stance markers of credibility and opinion
Bibliographic record
Abstract
This thesis is about gestural/textual stances of Canadian federal politicians – how and what two speakers/gesturers convey about their political messaging in 2019 before the Federal election. In particular the focus of this thesis was to understand their stances as their spoken utterances are marked by co-verbal manual and non-manual gestures. In short, the aim of this study was to collect and analyze their gestures. The central relationships under study were: 1. What are the gestures that are used most often to reflect a speaker’s stance; & 2. Is there a unique contribution of gestural stance markers to overall stance in the composite utterance, and if so, how much? Once the manual gestures were coded and organized, they were interpreted as they co-occurred with text. Additionally, stance marking was developed into a coding checklist of stance and non-stance marking of non-manual gestures based on findings in the gesture literature. I found that gestures with finger combinations tended to display speakers’ opinion-based stances more than signify points (BEATS) in their discourse involving their convincing/credibility-based stances. Second, basic hand orientation of palm-based gestures revealed an inverse relationship between the two speakers. This pattern emerged in two of the four basic orientations: Palm lateral and palm vertical. For Pierre Poilievre (hereafter ‘P’), gestures with a palm facing laterally or vertically tended to be used twice as often for stances conveying his credibility than for conveying his opinion. On the contrary, the same hand gestures with the lateral or vertical hand orientation conveyed Elizabeth May’s (hereafter ‘M’) opinion twice as often as they did for expressing her credibility. Additionally, I found that non-manual gestures such as smiles, shrugs, eyebrow raises, posture shifts, lean-ins, head-tilts, head shakes, and the division of gesture space all supported both speakers’/gesturers’ use of multiple viewpoints to convey their stances.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".