Bibliographic record
Abstract
Book Description: This collection celebrates emerging scholars in Indigenous studies, featuring student essays that explore Indigenous justice, ethics, and environmental justice, while highlighting a decade of collaboration with RAVEN, a legal defence organization. Named after the Respecting Aboriginal Values and Environmental Needs (RAVEN) nonprofit organization, The RAVEN Essays is an anthology that celebrates a decade of prize-winning student essays. Since 2012, RAVEN has awarded an annual essay prize to honour students who champion the vital importance of Indigenous rights and self-determination, both in Canada and globally. The essays featured in this collection highlight exceptional student work while reflecting on the evolving relationship between Indigenous politics and academia. From issues like fishing rights and the Trans Mountain Pipeline to challenges of sexism and conservation policy, these essays capture a transformative period in Indigenous struggles, offering insights that resonate far beyond the Canadian settler state. The anthology also includes contributions from prominent scholars such as Glen Coulthard, Dara Culhane, Michael Fabris, Sarah Hunt, and Heather Dorries. Five complementary essays explore various aspects of structural change, institutional constraints, and broader commitments to Indigenous knowledge within university settings. Aimed at readers in Indigenous law, environmental studies, anthropology, and geography, The RAVEN Essays is a book created by students for students, and by academics for the academy. Together, the contributors reflect on the powerful formation and enactment of Indigenous law, environmental stewardship, place-based knowledge, pedagogy, and literacy – both within the academy and in the broader community, across land, water, and culture.
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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.187 | 0.097 |
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".