Bibliographic record
Abstract
In 2017, in partnership with the UBC Longhouse and UBC Libraries,the Institute for Critical Indigenous Studies and the University of British Columbia Library began the annual “Honouring Indigenous Writers Edit-athon.” Each year, building out of events such as the Art + Feminism Wikipedia Edit-athon, our team of organizers work with Indigenous authors to improve the representation of Indigenous literatures online. We build consensual relationship with authors to revise Wikipedia pages, distribute organizer kits to interested collaborators, maintain an event dashboard, and host live readings from new and established Indigenous authors in Vancouver, Kelowna, and Alberta. The event itself is inspired by Daniel Heath Justice’s hashtag #HonouringIndigneousWriters, which he began on Twitter in 2015 to draw attention to the wide range of literatures available by Indigenous authors. With Justice’s consent, we build on his good work by furthering the reach of Indigenous literatures in digital and physical spaces. In this article, I suggest that #HonouringIndigenousWriters illustrates that any attempt to squarely demarcate boundaries between offline and online communities risks eliding the nuanced facets of relationality that are core to Indigenous literary studies. Bronwyn Carlson argues that in Indigenous engagements with the digital, there is often “no distinction between online and offline worlds; they are seamlessly enmeshed”. Productively blurring the boundary between online and offline worlds informs what critical and ethical and relational engagement in the digital must look like. Via a history of #HonouringIndigenousWriters, written from my perspective as one if its co-founders, I hope to illustrate how, as scholars of Indigenous literary studies, we can draw online and offline worlds into closer proximity and, as Warren Cariou urges us, find places to visit with stories.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".