Constructive Journalism in Real-Life Settings: The Case of Radical Ruralism in Les Basques (“Les Basques Autrement”)
Bibliographic record
Abstract
This research-creation project proposes the application of constructive journalism theory to the practice of digital multimedia documentary production. It uses rural Quebec’s Les Basques region as a creative testing ground to show how theoretical models of constructive journalism can be used to tell stories of radical ruralism in the region. To date, very few examples of how to apply the theoretical teachings of this model to digital multimedia documentary production exist. Constructive journalism is a solutions-oriented model that focuses on positive emotions and reports on affirmative, inspiring and often untold narratives, and aims for engagement and co-creation. Mobilizing elements of previous alternative models such as peace journalism, civic/public journalism and solutions journalism, constructive journalism attempts to respond to declining trust levels and audience disengagement in the news by proposing a model that seeks to inspire hopefulness and resilience. This research-creation project takes a multimedia approach and uses video, photography, audio and text in its digital documentary component, Les Basques autrement. Using the documentary produced, this research also provides a constructive journalism “how-to” guide accompanied by notes and reflections from the field to discuss the implications of applying the model’s theoretical framework in a real-life setting.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".