Remote control: rethinking government information subsidies and media relations in post-pandemic Atlantic Canada
Bibliographic record
Abstract
The COVID-19 pandemic profoundly impacted how government officials and the press interacted across Canada, whether by introducing new dynamics, or exacerbating existing ones. Given the reduced physical proximity between political journalists and premiers and subsequent increase in the use of remote communication methods, along with the evolving media and political landscapes, this thesis examines why interactions between provincial journalists and premiers in Atlantic Canada have changed since the onset of the pandemic. I hypothesize that this change stems from strategic shifts toward cost-effectiveness concerning resources such as time, money, and staff. This thesis investigates the role of technological developments in media-state communications in Canada’s Atlantic region. It takes a mixed methods approach, drawing from in-depth interviews with current and former political journalists and communications staff in Premiers’ Offices in each of the Atlantic provinces, and an examination of provincial media advisories. Using Oscar H. Gandy’s theory of information subsidies as an analytical lens, this thesis finds that both actors have changed the ways they communicate, including the creation and consumption of information subsidies, in order to maximize limited resources in a post-COVID environment. Though trends towards remote and digital communication were already underway, the pandemic hurried their adoption and normalization, irrevocably changing the environment both journalists and premiers operate within. While improving accessibility for journalists, reliance on remote technologies introduces concerns for government accountability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".