Bibliographic record
Abstract
Media bias is a reality of the infoglut we are bombarded with every day. However, we often consider bias to be consigned to the textual realm of information. I argue that anything human-mediated holds bias, including photographs. Because of this, I propose reading the performance of Indigenous-led environmental activism through media representation, specifically photographs used in media coverage of Indigenous environmental activism. This paper considers open-access media photographs of Indigenous-led environmental protests, such as the Kanehsatake Resistance (1990) and Wet’suwet’en Blockade (2020), as springboards for practicing ethical reading. As a settler-scholar, this work is mostly geared towards a settler-scholar or non-Indigenous audience interested in Indigenous literary studies, as a way to find tools to engage in this scholarship. The purpose of this article is to elucidate media bias as a way of informing our individual teaching and learning practice, as well as shaping how we engage with and talk about Indigenous issues. While all public activism engages with some levels of performance, the performance itself and larger narrative being told by the activists is filtered through who is able to tell the story. Here, I use a methodology that I am developing as part of my ongoing dissertation work, Critical Dispositioning, which is an ethical reading praxis designed for settlers to use when engaging with Indigenous literatures. Critical Dispositioning requires community-specific reading of Indigenous materials and rejects settler imposition or appropriation of Indigenous voices and texts. This work is essential in building anti-racist practices and equity, diversity, and inclusion into the classroom space, as well as a tool for consideration when building syllabi.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".