Missed opportunities for community engagement: An examination of the government-funded Local Journalism Initiative
Bibliographic record
Abstract
This article examines the Local Journalism Initiative (LJI), initially a $70-million, five-year program of the Canadian government to fund new reporting positions in existing newsrooms across Canada, with the goal of increasing the amount of civic journalism. Using a mixed methods approach, we analyzed the language in almost 100 publicly available documents, conducted interviews with 11 participants and did a content analysis of 240 stories to examine how newsrooms defined the news desert they were trying to fill, whether work was civically focused and professionally produced, and what the LJI tells us about what kind of journalism the market can’t fund. In comparing the implementation of the program to an emerging set of best practices in journalism, we argue that the LJI represents a missed opportunity to help newsrooms evolve to better focus on the kind of information their communities need. We conclude with the one outlier in our sample, a community-access television station, that does make efforts to engage its community. Keywords: local news, qualitative methods, case studies, Local Journalism Initiative, political economy of news
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 | 0.036 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.030 | 0.030 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".