Bibliographic record
Abstract
A Different Kind of Birthday was one of my school assignments that I had produced last semester in my 3rd-year FILM documentary class with professor Tony Lau. The assignment's goal was to create a short and compact documentary that would narrate a unique, thought-provoking, yet relatable story. Being a Chinese-Canadian-Adoptee, I wanted to make a short film that told my unique story and perspective about being adopted and how my personal experiences may differ from others. When I turned 20 years old last April, I became very emotional and sad, reflecting on how my adoptive family (which I call my real family) did not know me or were not with me on the day I was born in China. Also, simple questions about one’s beginning, such as the exact time one was born or how much they weighed as a newborn, has always been a mystery for me; yet for others, they know this part of themselves very well. Not only did I want my short film to show my unique journey, but also to help connect and relate to others regardless of their age, gender, race, sexual orientation, and religious background to my film project. In other words, I want people to reflect on their own personal stories and how their birth stories are rare and beautiful. A Different Kind of Birthday essentially shows that everyone is unique and has something extraordinary about how they came to be in this world.
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 | 0.004 |
| 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 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".