Bibliographic record
Abstract
In Canada, in 2015 the Truth and Reconciliation Commission of Canada (TRC) brought forward to all Canadians the devastating and lasting effects of residential schools on Indigenous peoples. The TRC published the Calls to Action to redress the legacy of residential schools and advance a process of Canadian reconciliation, which renewed efforts within many sectors. While many post-secondary institutions were already working with Indigenous communities, the TRC’s Calls to Action resulted in project funding from a variety of sources that aimed to address the Calls to Action.From 2016 –18, the BCcampus Indigenization Project formed a collaboration between the Ministry of Advanced Education, Skills and Training, BCcampus, an Indigenization Project Steering Committee of eight Indigenous educational leaders and thirty Indigenous and ally writers representing fourteen BC post-secondary education institutions to create Indigenization resources. This article highlights the project and how BCcampus approached learning about reconciliation and Indigenization while embarking on the creation of a series of online Indigenization guides for post-secondary institutions.
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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.073 | 0.054 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.005 | 0.035 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".