Bibliographic record
Abstract
Academia has been dominated by European/settler ways of knowing while denying the existence and validity of Indigenous epistemologies, science, and philosophies. Post-secondary structures were not built to be inclusive spaces, they were built without Indigenous voices or considerations and often housed individuals and departments who have perpetuated research harms towards Indigenous peoples. These spaces have been and remain a place of privilege where few Indigenous knowledge holders manage to become established. In this article, calls for action to transform academia into an inclusive space through ongoing conversations on the Truth and Reconciliation Commission (TRC) report and the widespread theme of taking on Equity, Diversity, and Inclusion (EDI) conversations will be examined. Without conversations around tools and ideas about practical steps to take in our daily work navigating academic spaces, we maintain colonial structures by being complicit and avoid the question of where the Indigenous voices are found in these spaces.
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 | 0.031 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.144 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".