Engaging Communities in Newfoundland and Labrador Journalism: A Case Study of News Coverage of Project Nujio’qonik in Port au Port.
Bibliographic record
Abstract
In Newfoundland and Labrador, the decline of local news is problematic for rural communities and democracy. Considering this decline, this thesis focuses on the rural community of the Port au Port Peninsula, as it faces the introduction of a massive wind farm project (Project Nujio’Qonik). This thesis explores the evolution of journalism from traditional concepts such as watchdog journalism, to engaged and community-centered journalism. This thesis seeks to answer three research questions: How do community members in Port au Port feel about journalism efforts and news reporting in the context of a news shortage? How can journalism adapt to address the loss of local news in the province to better engage communities in the face of significant developments like Project Nujio’Qonik? What perspectives on the wind farm are most prominent in Newfoundland and Labrador news coverage, and to what extent are community voices represented? Qualitative interviews with community members from Port au Port were used to answer the first two research questions, and a basic analysis of the voices quoted across the wind farm project’s news coverage from August 2023 to August 2024 was conducted to answer the third question. This thesis finds that community members are not content with reporting efforts, due to perceived bias and not enough local news. Community members shared that they want more factual, unbiased reporting, that involves the community more in the process. They also want more local journalism physically present in the community. The analysis also found that government and corporate voices were quoted significantly more than community voices. Therefore, this thesis suggests that engaged and community-centered journalism techniques, by involving the community more in the reporting process, could work to restore community members' satisfaction with reporting.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".