Podcasting protocols: Land acknowledgement in outlining a process for decolonial reflexivity and audio stewardship
Bibliographic record
Abstract
As journalists and media-makers in Canada work towards reconciliation with Indigenous peoples, there is increased discussion on how to work respectfully with Indigenous communities, stories and knowledge. However, for podcasters and audio journalists, there are still limited resources on guidelines and best practices. This article considers several resources which foster decolonial frameworks for mediamaking, from media production guides to broader Indigenous methodological frameworks, and discusses how they can inform media production in these contexts. The authors then discuss their experience working on the Canadian Mountain Podcast – a series that shares mountain-related research from academic and Indigenous perspectives – and the steps the team took to decolonize their methods and work respectfully with different forms of knowledge. Finally, this article looks at the team’s use of developing land acknowledgements and how this practice provided a space to reflect on their journalistic practices and adjust their processes as audio stewards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".