Bibliographic record
Abstract
The purpose of this project is to create a workshop utilizing the latest theoretical frameworks and pedagogical approaches to create space for non-Indigenous Canadians to stumble through controversial topics related to Reconciliation. The impetus of this project stems from the Truth and Reconciliations Calls for Action (Truth and Reconciliation Canada, 2015), which advocates for Reconciliation work to be funded, researched and implemented for non-Indigenous Canadians. If Canada as a whole is to make progress towards Reconciliation between non-Indigenous and Indigenous citizens, space must be nurtured where non-Indigenous Canadians can grapple with complex issues of decolonization and Reconciliation in a transformative environment. To move forward towards Reconciliation, the non-Indigenous population must start/move along the path of decolonization. Further, non-Indigenous Canadians need to understand that assimilation tactics and colonial thinking are not past tense. They are held up by our institutions, culture and assumptions. Although much work is being done in the K-12 sector to educate youth, there are a large group of adults that have limited access (if any) to safe and honest space where transformative learning can occur.
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 | 0.021 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.059 | 0.025 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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".