All About Strong Alliances: First Nations Engagement in the Federal Election
Bibliographic record
Abstract
[para. 1-2]: "For many members of First Nations communities, the basic rights of First Nations and the protection of their environment were at stake in the 2015 Canadian federal election. The thought of another Conservative majority was too much to bear for many of them. To prevent this Conservative majority from happening, First Nations and like-minded Canadians used the alliances they forged under the Idle No More movement to rally the vote against this possibility. Idle No More was the largest social movement Canada has ever seen. For over six months in 2013, it captured media headlines around the world. To members of this movement, Stephen Harper’s focus on security was based on a campaign of fear, not facts. The Conservative government’s Anti-Terrorism Act (Bill C-51) had the potential to make Canadians terrorists for merely expressing their dissent. The result was public outcry from many segments of society, including some former prime ministers, former Supreme Court of Canada justices, Canadian Security Intelligence Service (CSIS) officials, lawyers, and academics. How Bill C-51 might be used to suppress another Idle No More movement was a source of concern."
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.048 | 0.007 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".