Coming Home: A Journey Through the Trans-Systematic Knowledge Systems
Bibliographic record
Abstract
In the Exchanges, we present conversations with scholars and practitioners of community engagement, responses to previously published material, and other reflections on various aspects of community-engaged scholarship meant to provoke further dialogue and discussion. In this section, we invite our readers to offer their thoughts and ideas on the meanings and understandings of engaged scholarship, as practiced in local or faraway communities, diverse cultural settings, and in various disciplinary contexts. We especially welcome community-based scholars’ views and opinions on their collaborations with university-based partners in particular and engaged scholarship in general. In this issue, we present a discussion between Sa’ke’j James Youngblood Henderson and Dr. Leroy Little Bear from November 2020.
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 | 0.888 | 0.587 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.738 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.728 |
| 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; both teacher heads agree on what is shown here.
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".