Connection Building through ʻŌlelo Hawai‘i, the Hawaiian Language
Bibliographic record
Abstract
As scientists, we are lifelong learners who tend to seek new ways to connect with dynamic aspects of the natural world and find meaning. Often, without recognition, our interpretations are informed by language. English is the preferred language as the core communication mechanism in many scientific fields and has roots in Latin and Greek. Due to Eurocentrism and colonialism, the requirement for English fluency has generated barriers to information exchange and a better understanding of perspectives and culture (Berenstain et al. 2022). Hawai‘i is one of three U.S. states where the official languages (ʻŌlelo Hawai‘i and English) include an Indigenous language. The Respectful Meetings Working Group (RMWG) encourages attendees of the 154th AFS Annual Meeting in Honolulu, Hawai‘i, to take a moment to learn about ʻōlelo Hawai‘i, the Indigenous language of Ka Pae ʻĀina (Hawai‘i) and reflect on how this experience can promote cultural understanding through insights into linguistic diversity (Chiblow and Meighan 2022).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".