‘Such Cold, Dispassionate Terms Fail Us’: Reading <i>Hansard</i> as an ‘Archive of Feelings”
Bibliographic record
Abstract
Abstract This article proposes a distinctly affective methodological approach to Hansard transcripts using feminist affect theory and Pierre Bourdieu’s reflexive methodologies. Expanding the ‘toolkit’ of analysis available to parliamentary and legislative researchers to understand the political worlds we inhabit, I contend scholarship engaging with Hansard is shaped—but not limited—by the format and editorial decisions that inform its publication. Feminist scholarship on affect enables both (i) generative methodological approaches to so-called limitations of Hansard, and (ii) empowers a critical advancement of how researchers can engage Hansard as data. Considering the Hansard corpus as a process (rather than a thing) enables analyses to begin not with the question of can affect and emotion be present in transcripts, but rather how can emotion and affect proliferate throughout a genre designed and mediated in such a way as to occlude these emotional and affective speech patterns. I conclude by offering methodological strategies through an exploration of research vignettes drawn from my work with Canadian provincial Hansards.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.032 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".