La Réconciliation - en cours? Reconciliation - in progress?
Bibliographic record
Abstract
For my Master’s in French Mémoire, or thesis, I will be using the Truth and Reconciliation Commission’s, TRC, 94 calls to action as a central document. I will study 6 books published by Canadian Indigenous authors, with three in English and three in French. The main question I want to explore is if the 94 calls to action, published in 2015, have had any effect on Indigenous and non-Indigenous peoples in Canada, whether that effect is of positive progress, negative, or little to no change at all. In order to do this, I will first introduce my texts, giving some details regarding the author, their First Nation, where and when the book was published, and a small resume of the content. I am selecting three texts that were published before November/December 2015 and three published after that same date to explore if there is any influence from what the texts have to say and the final report, as well as if any noticeable dialogue between the report and the texts after 2015 take place, showing any indication of possible progress. In order to achieve all of this, I will give some context on the effects of colonisation in Canada, and what the TRC is and how it came to fruition. I will read all 6 of my chosen texts and decide which of the 94 calls are most pertinent or come up most frequently, with in-depth analysis to help prove or disprove the document’s relevancy for progress.
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 | 0.025 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.020 | 0.043 |
| Scholarly communication | 0.028 | 0.026 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".