Bibliographic record
Abstract
This special issue addressing the theme of “Indigenous and Trans-Systemic Knowledge Systems” seeks to expand the existing methods, approaches, and conceptual understandings of Indigenous Knowledges to create new awareness, new explorations, and new inspirations across other knowledge systems. Typically, these have arisen and have been published through the western disciplinary traditions in interaction and engagement with diverse Indigenous Knowledge systems. Written by Indigenous and non-Indigenous scholars, and in collaborations, the contributions to this issue feature the research, study, or active exploration of applied methods or approaches from and with Indigenous Knowledge systems as scholarly inquiry, as well as practical communally-activated knowledge. These engagements between Eurocentric and Indigenous Knowledges have generated unique advancements dealing with dynamic systems that are constantly being animated and reformulated in various fields of life and experiences. While these varied applications abound, the essays in this issue explore the theme largely through scholarly research or applied pedagogies within conventional schools and universities. The engagement of these distinct knowledge systems has also generated reflective, immersive, and transactional explorations of how to foster well-being and recovery from colonialism in Indigenous community contexts.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".