Table1_Power and Positivity: Psycholinguistic Perspectives on Word Valence in Canadian Parliament.pdf
Bibliographic record
Abstract
<p>Politicians are skilled language users who deploy words strategically and pay close attention to the emotions that those words evoke. We examined the emotional characteristics of over 92 million words spoken by Canadian Members of Parliament between 2006 and 2021. The analysis brought together the Warriner, Kuperman, and Brysbaert (Behav. Res., 2013, 45, 1191–1207) database of valence (positivity) ratings for English and the Canadian Hansard, which contains a transcription of parliamentary speech. Results revealed that the positivity of words used by politicians in parliament was significantly related to both political and social variables. Politicians increased the positivity of their language after the onset of the COVID-19 crisis. Within the time of the crisis, word positivity was linked statistically to month-by-month case counts, indicating a very fine-grained sensitivity to social realities. Our analysis also revealed a fine-grained sensitivity of word valence to political realities. As expected, parties in power used more positive language than those in opposition. In addition, our analysis revealed that individual parties have characteristic levels of word positivity and that those levels change in accordance with political changes as specific as whether or not the party in power holds a majority of seats in parliament. These findings suggest that the emotional properties of words used by Members of Parliament are reliably indexed to sociopolitical dynamics. The findings also suggest that the methodology of linking individual word ratings to Hansard Documents (which are used to document Parliamentary activities in over 25 countries) can provide a key tool for the understanding of specific crises such as the COVID-19 global pandemic as well as more general social and political trends across countries and languages.</p>
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.652 | 0.003 |
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".