A Question of Respect: A Qualitative Text Analysis of Canadian Parliamentary Committee Hearings on PCEPA
Why this work is in the frame
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Bibliographic record
Abstract
<b>Project Summary</b>: The overarching research question that we address in our paper, “A Question of Respect: A Qualitative Text Analysis of the Canadian Parliamentary Committee Hearings on The Protection of Communities and Exploited Persons Act (PCEPA),” forthcoming Canadian Journal of Political Science/Revue canadienne de science politique (December 2017), centers on whether parliamentary committee members treated witnesses fairly and respectfully.<p/> <p>To address this question, we engaged in a qualitative text analysis of the hearing transcripts of both the Standing Committee on Justice and Human Rights and the Senate Standing Committee on Legal and Constitutional Affairs on this bill that took place in the summer and fall of 2014. We found in this study that, on the whole, the vast majority of questions met this baseline, but that committee members were biased toward witnesses in agreement with their position and against witnesses in opposition to it. Our approach was based on grounded theory, and we inductively developed codes from an interpretation of the data. In this appendix, we present our coding scheme, including key assumptions, units of analysis, conceptualization and coding process, reliability and agreement measurements, and core and evaluative codes. We hope that other qualitative researchers will use and develop our codes.<p/> <p><b>Data Abstract</b>: Our data took the form of PDFs of official English-language transcripts of parliamentary hearings by both the House and Senate committees on Bill C-36. More specifically, our data units were questions posed by committee members to witnesses as articulated in the hearing transcripts. <p>The hearings on Bill C-36 took place in July 2014 (House) sand September and October 2014 (Senate), and the transcripts are publicly accessible on government websites (full list of links provided in documentation). Our data collection strategy involved downloading PDF versions of each of the Commons and Senate hearings on the bill. We conducted an initial read of the transcripts to identify questions posed by committee members to witnesses (please see our coding scheme for a detailed discussion of how we identified questions for analysis). We organized the questions (i.e., our units of analysis) by assigning to each a unique number. This enabled us to systematically code each question in terms of its content, tone, and nature (see coding scheme for more details on our coding definitions).<p/> <p>This deposit consists of our coding scheme, which we hope will provide other researchers with definitions of respectful/disrespectful, positive/negative/neutral tone, and sympathetic/combative/fair questions and with an approach to conducting qualitative text analysis of legislative hearings. It also consists of links directly to the hearing transcripts for Bill C-36, as well as the full-text version of all the transcripts we analyzed.<p/>
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.012 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it