Laying the groundwork for decolonization, Indigenization and reconciliation in pharmacy
Bibliographic record
Abstract
Portrait of a Warrior" is a symbolic evolution of my journey as an artist/person and the rediscovery of my past merging with the present.The face is a representation of the mothers and grandmothers who witnessed the pain of having their children and grandchildren taken away from them.Having experienced this firsthand, I have come to understand that this chapter in many children's lives would forever change them.Gone was their innocence, their childhood happiness, their language, culture and traditions.The very fabric of their lives would be torn, and everything they held dearest would be ripped away.By the time this chapter ended, these children would emerge as victims and survivors of abuse that was seen and unseen.Some would perpetuate a vicious cycle that would consume their very lives and destroy the structure of the family itself.We have now entered the era of reconciliation, and it is time to embrace this concept personally, socially and creatively.This piece is a reminder to myself and to others who have experienced these injustices that we must learn to accept what we couldn't change then, but we can now move ahead to change how society and our own families see us. -Kevin PeeacePortrait of a Warrior.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.018 | 0.027 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".