Bibliographic record
Abstract
he Swampy Cree language I use in this book is the language I grew up with.It was passed down from generation to generation through oral tradition, and it makes us who we are.Our language was not meant to be written.The first publication of it using Standard Roman Orthography (SRO) was in the 1850s, a biblical translation.In this book, I have written the Cree language using that orthography, which uses macrons.I understand that they are used for non-Cree speakers who wish to practise speaking Cree.In my own Cree-writing experience, I am able to read the Cree wording without sounding it out to get the meaning, though during my writing I had to sound the words out to make them come alive.I also had to do that to ensure grammatically correct usage.My understanding is that in Saskatchewan and Alberta most Cree-language writing is done in SRO with the use of macrons.I have also provided approximate English translations of the Cree words and phrases, but I have discovered that translation of languages is tougher than what most people think.I
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".