Feast of Nations organizers take pride in making international cuisine just right
Bibliographic record
Abstract
Feast of Nations organizers take pride in making international cuisine just right 52nd annual event set for Saturday, Feb. 15 Srijan Konwar has been to a couple Feast of Nations celebrations in the past as a guest, but this year the pressure was on for the University of North Dakota psychology major and native of India. The first order of business for Konwar was to make sure the food that is served this time around is as authentic as possible and, of course, tasty. He said that getting it just right has been a challenge in the past. In recent years, feast organizers have gotten together with Alerus Center chefs, who, naturally, are more accustomed to American style fare, for a practice run – a taste test, if you will – to make sure that the cultural integrity of the cuisine is intact and so taste buds pop on event night. That's what happened again, this year, on Jan. 15, a full month before this year's actual feast. Konwar and fellow organizers, representing four countries and decked in traditional cultural garb, met with the Alerus Center catering staff members for their first taste of the foods that will comprise the Feast of Nations' five-course menu. Konwar and his crew offered suggestions for more authentic ingredients and tips on food choices to make the feast more enjoyable for those of varying international customs. "When we went there for the taste testing, we were able to rectify a lot of the problems before they happened," Konwar said. "We got pretty close this year – as close as it could possibly get to authentic. I think, this time, everyone will say that the food is out of this world." They also suggested adding more vegetarian options to the menu and using flavored yellow rice instead of white rice in one of the traditional dishes. The international taste testers also ensured Alerus chefs understood not to use pork broth for the soup lest it be unsuitable for Muslims and people of other cultures to consume, according to Konwar, who, despite being born in India, spent much of his life in Oman, the U.S. and a few years in the United Arab Emirates. "The Alerus Center does a good job accommodating our requests," said Eller Bonifacio, a UND alumnus from Angeles City, Philippines, who is the creative director for marketing for the Feast of Nations. "It can be challenging to produce international meals of such mass but they are always there to deliver every year." Some of the food highlights for this this year's event included Russian pierogies (beef and vegetarian), Tuscan white bean soup, Tunisian slata mechouia salad, Nepali matar paneer with plain rice, Indian butter chicken, and a Scottish banoffee pie for desert. "We try to mix up the food choices every year so that it isn't always the same thing," Konwar said. Each dish was labeled and menus provided guests with a short description of the foods so that they knew what they were eating and how they might prepare the same thing at home. The list of entertainers that were at this year's feast included Sol de Espana, a club of Spanish folk dancers; the Manitoba Korean Dance Troupe; Victor Lopez & Son; and UND student performances. "I always look forward to meeting different people from around the world and seeing the performances on stage," Bonifacio said. "It's really a great event every year for people to enjoy different foods and entertainment from different countries," Konwar said. David Dodds University & Public Affairs writer
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.141 | 0.032 |
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".