Improving climate change reporting in Indigenous communities with \nconciliatory journalism
Bibliographic record
Abstract
This research-creation thesis explores how climate change reporting in Indigenous communities can be improved by integrating elements of conciliatory journalism. This involves the active use of conciliatory journalism principles, as well as visual and reconciliation journalism, and then reflective observation on the reporting undertaken in an Indigenous community. This work draws inspiration from Hautakangas & Ahva (2018) and Salas & Stevens (2021). \n \nThe project involved travel to Inukjuak, a community in Nunavik, Quebec, to carry out these observations. Inukjuak is located roughly 1500 km north of Montreal and is a community only accessible by boat or plane. The team for the trip involved three other people: Journalism professor Aphrodite Salas, the team leader; undergraduate student Luca Caruso-Moro (who also works at CTV Montreal), and undergraduate student Virginie Ann (who at the time worked at Canadian Press). The research-creation components involve the collaborative creation of a short documentary “Innavik: Leading the Way to a Clean Energy Future”; a video explaining Inukjuak’s past titled “Simeone Nalukturuk, in his own words” (edited by Luca Caruso-Moro); a text piece (written by Virginie Ann); and the production of a conciliatory-informed photography portfolio (which I created). The written component of this thesis involves a reflection on the reporting notes taken in the field, as well as a reflection on the documentary creation process. The discussion is an autoethnographic analysis of the process behind reporting a piece of conciliatory-informed journalism in the community of Inukjuak. It also looks at the challenges behind creating this piece of visual journalism in the editing room. The goal is to robustly explain how a conciliatory-informed approach was employed both in the field and during the journalism creation process. \n \nThe documentary and photography centres on the self-determination of the community to overcome the challenges of building a run-of-river hydroelectric project in the Arctic to reduce or eliminate their reliance on diesel fuel imports.
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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 | 0.031 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".