Returning to Hanoi After 7 Years... Is THIS the Same City?! (OGG Audio)
Bibliographic record
Abstract
Original Title: Returning to Hanoi After 7 Years... Is THIS the Same City?! Channel: Joen & Amalie Duration: 20:55 Views: 2,023 Likes: 140 Comments: 16 Published: 2025-08-19T16:00:14Z YouTube URL: https://www.youtube.com/watch?v=4FLjSo_WdmQ Original Description: ❤️ Subscribe to Amalie's new channel: @DearJamilaa - She uploads twice every week! ❤️ ✨ Back in 2017, we visited Hanoi, Vietnam for the very first time — and it instantly becameone of our best travel experiences ever. The city felt so authentic, raw, and beautiful in its own way. We’ve been dreaming of returning ever since… and now, 7 years later, we’re finally back! But has Hanoi changed? After a huge real estate boom and a growing middle class, the city has transformed rapidly. From the bustling Old Quarter and hidden alleyways to the legendary egg coffee, street food, and vibrant night markets, we’re exploring how much Hanoi has changed — and what remains the same. This is Part 1 of our Hanoi Trilogy, so grab a coffee (or an egg coffee if you can!) and join us as we revisit old memories, discover new local gems, and dive deep into the heart of Hanoi in 2025. 🌏 In this video: • Checking into a beautiful boutique hotel in Hanoi • Our first impressions returning after 7 years • ... This OGG audio file was extracted and optimized from YouTube video for educational and archival purposes. Audio format: OGG Vorbis (optimized for voice/speech) Audio file size: 4,792,456 bytes Archived on: 2025-08-20 04:09:25
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.573 | 0.258 |
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; the direct Gemma label and the distilled Codex classifier 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".