Government of Canada Social Media Monitoring and Its Role in Public Environment Analysis
Bibliographic record
Abstract
Over centuries, governments have employed various mechanisms to measure and understand their populations.As information and computer technologies progressed in the 20 th century, governments adopted new ways of communicating with citizens and gathering statistics to inform decision making; at the same time, digital platforms such as social media became increasingly integral to social life.This dissertation examines the Government of Canada (GC) and its use of social media monitoring to understand the public environment, specifically within the context of communications work.It situates this analysis within a broader context of public administration paradigms and in the affordances of social media monitoring tools, in order to understand how this monitoring constructs a particular understanding of the public.The dissertation employed interpretive content analysis to conduct primary research, with data consisting of expert interviews, survey results and GC documents and policies.Researcher positionality was also a core aspect of analysis; prior knowledge and experience of social media monitoring inspired the subject of study and informed research questions.It equally afforded the researcher access to the 71 participants interviewed in the study, and the opportunity to share research findings with the Privy Council Office.This research found that participating GC departments often sought to use social media monitoring to support existing work done in communications branches, and in many ways approached monitoring with aims that align with tenets of Digital Era Governance.At the same time, existing conditions within the GC bore marks of New Public Management-style governance that limited the GC's capacity to undertake iii monitoring in a methodologically sound and critically engaged way.Communications branches were not necessarily equipped to understand and address the challenges of using social media data, particularly with regards to privacy requirements.They also depended heavily on tool vendors for the technologies and training, which facilitated the adoption of dataist rhetoric already being espoused by public and private sectors alike.Ultimately, the public environment analysis conducted by GC communications branches through social media monitoring is partial and biased, while largely contravening privacy requirements.This is particularly significant as departments look to expand monitoring and its applications in government.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".