Pulling together : a guide for leaders and administrators
Bibliographic record
Abstract
Inspired by the annual gathering of ocean-going canoes through Tribal Journeys, 'Pulling Together' created by Kwakwaka'wakw artist, Lou-ann Neel, is intended to represent the connections each of us has to our respective Nations and to one another as we Pull Together.Working toward our common visions, we move forward in sync, so we can continue to build and manifest strong, healthy communities with foundations rooted in our ancient ways.Thank you to all of the writers and contributors to the guides.We asked writers to share a phrase from their Indigenous languages on paddling or pulling together… 'alhgoh ts'ut'o ~ Wicēhtowin ~ kən limt p cyʕap ~ si'sixwanuxw ~ ƛihšƛ ~ Alh ka net tsa doh ~ snuhwulh ~ Hilzaqz as q̓ iǵǔaĺa q̓ uśa m̓ ańaǵǔala wiẃ̓ uýalaxšṃ ~ k'idéin át has jeewli.àat~ Na'tsa'maht ~ S'yat kii ga goot'deem ~ Yequx deni nanadin ~ Mamook isick Thank you to the Indigenization
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.083 | 0.117 |
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".