Social Media Branding of Ka Pa Olapa O Na Pua Kukui
Bibliographic record
Abstract
This project is a multimedia project showcasing Ka Pa Olapaonapuakukui. I will produce video, photo, and graphic content to be used on their social media accounts and websites. I will also be creating a graphic outline and posting schedule to be given to future Marketing and PR/Social media managers for the organizations. Ka Pa Olapaonapuakukui is based in Vancouver, WA. This organization is a branch under Piko Eha which is a non-profit organization that promotes Pacific Island culture. These organizations are relatively new and have had difficulty in building and maintaining their social media presence and increasing their audience outreach. As a native Hawaiian now living in the PNW, I understand the importance of maintaining ties to the culture that I grew up around. With this organization being an outlet for those of Pacific island descent or ties to the Pacific Islands, I feel that it is important to help an organization that connects them to the culture wherever they are. I will be interviewing the coordinator and founder of these organizations and conducting interviews used for media purposes and also to ensure that I am promoting the organization in the right way. By revamping and creating platforms for the organization to reach a wider audience, I also hope to create a “home” for people to be able to find the right resources when learning or reconnecting with Pacific island culture.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".