Journalistic Pathfinding: How the Parliamentary Press Gallery Adapted to News Management Under the Conservative Government of Stephen Harper
Bibliographic record
Abstract
Commentary on the contemporary interface between the media and governments often portrays outnumbered reporters as willingly accepting information subsidies as a way of meeting the demands of the 24/7 multi-platform newsroom.But this view fails to take into account the impact on journalistic routines of more extreme forms of government news management, which block access to information and to politicians rather than merely packaging or "spinning" them favourably.The experience of the parliamentary press gallery in Ottawa vis-à-vis Stephen Harper's government offers an excellent opportunity to take a closer look at the practical realities of political journalists confronted with stringent government news management tactics.A rupture in the historic role relationship between the gallery and the Prime Minister's Office resulted in journalists adapting their techniques.They became pathfinders seeking out new routesalternative human and data sources -to reach the information they needed to write their stories and prepare broadcasts.having been away from university for 16 years doing hard news, the academic ideas weren't exactly flowing freely.In the first master's course I took in 2011, Media and Society, graduate supervisor Susan Harada provided students with a thought-provoking range of writings to absorb and study.One of those was Kirsten Kozolanka's examination of the role strategic communications played in the sponsorship scandal.This article spoke to me.As a member of the press gallery, frustrated with increasingly politicized information products from the public service and restricted access to politicians, the article served as a springboard into this thesis.Kirsten Kozolanka went on to become my thesis supervisor, an ideal match as her ongoing research into the area of political communication has provided me with exactly the type of expertise and enthusiastic guidance that I required.I must also acknowledge the support and encouragement of Susan Harada, who was a sounding board and helped me to better conceptualize the thesis.I thank the 14 members of the parliamentary press gallery who agreed to take time out of their busy schedules to talk to me about these issues.It came as no surprise that their comments were always thoughtful, never self-reverential, and often profound.I must also acknowledge the patience and support of my family -Greg, Gabriela and Amaya -who put up with many distracted evenings and weekends as I tried to fit school into the nooks and crannies of free time left over after work.This is dedicated to them.Limitations ......
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.030 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".