Sara Kehaulani Goo — Kuleana: A Story of Family, Land, and Legacy in Old Hawai'i - with Niala Boodhoo
Bibliographic record
Abstract
Music and hula by Halau Nohona Hawai'i . From an early age, Sara Kehaulani Goo was enchanted by her family's land in Hawai'i. The vast area on the rugged shores of Maui's east side--given by King Kamehameha III in 1848--extends from mountain to sea, encompassing ninety acres of lush, undeveloped rainforest jungle along the rocky coastline and a massive sixteenth-century temple with a mysterious past. When a property tax bill arrives with a 500 percent increase, Sara and her family members are forced to make a decision about the property: fight to keep the land or sell to the next offshore millionaire. When Sara returns to Maui from the mainland, she reconnects with her great-uncle Take and uncovers the story of how much land her family has already lost over generations, centuries-old artifacts from the temple, and the insidious displacement of Native Hawaiians by systemic forces. Part journalistic offering and part memoir, Kuleana interrogates deeper questions of identity, legacy, and what we owe to those who come before and after us. Sara's breathtaking story of unexpected homecomings, familial hardship, and fierce devotion to ancestry creates a refreshingly new narrative about Hawai'i, its native people, and their struggle to hold on to their land and culture today. Sara Kehaulani Goo is a journalist and senior news executive who has led several news organizations including Axios , NPR and The Washington Post . She is the former editor-in-chief at Axios, where she launched the company's editorial expansion into national and local newsletters, podcasts and live journalism. Before Axios , she led online audience growth as a managing editor at NPR, overseeing the newsroom's digital news operation. Goo also served as news director at The Washington Post , where she also served as a business editor and reporter. Originally from Dana Point, California, she graduated from the University of Minnesota's journalism school. She lives in Washington, D.C. Goo will be in conversation with Niala Boodhoo , an American journalist, podcast and events host. You can hear her many Fridays on NPR stations hosting the Friday News Roundup on 1A. Currently the founder of Glad You’re Here Productions, she's launched shows for public media and podcasts, including Axios Today and 1 Big Thing, and hosts live news events for organizations that have included Axios, the World Bank, Ascend at the Aspen Institute and the Religion News Service.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.004 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".