Bibliographic record
Abstract
This short reflection with a photo is based on an emotional moment I had during my last visit to Kuwait, where I was born a third-generation stateless person, and later emigrated from to Canada, where I currently reside. It is an expression of the emotional and social manifestations of living between two countries and experiencing the loss of time with family and friends. It is an expression of the yearning for social connections that I left behind. It is also an attempt to reconcile with the experiences of forced migration and distance, which are not always successful. I used to visit Kuwait annually, but when COVID-19 hit, it interrupted these frequent visits. Up until recently, I had not visited in four years. But I stayed connected with my family and friends virtually, though WhatsApp, Instagram, Twitter, Zoom. When I finally went this winter, I was filled with overwhelming emotions. It had been too long, and I felt as though things have changed. But it was really me who has. Being away for such a long time has created a gap that I am still trying to understand. I was never troubled by distance. In fact, I loved it because it gave me the space to learn more about myself and explore the world. I thought I was okay with the distance for the longest time. But I learned how much I was craving the warmth of close connections, the in-person quality time, the long night chats, the morning coffees, the brunches by the beach, and the hugs I received from loved ones. I leaned that this distance meant missing out on making memories with loved ones. And losing so much time that can never be regained. I realized that when I was glued to my phone every day for hours, it was because I wanted to stay connected, and not be forgotten because distance can do that. I have already felt so many friendships fade away because of the distance, and now I find myself trying to hold on to those I have left. Yes, I made new ones virtually. And I had the chance to meet them in person this time. And it was beautiful. But now, I am leaving, again. I am more now of how this distance impacts me, and my relations. This time, I was very sad to leave. I wasn’t ready to leave just yet. I did not get enough time to be with my loved ones. It was a month, but it felt so short. I tried to squeeze all the meetings, squeeze all the love I craved, but it was so much love that just didn’t fit in such a short time. I had to leave eventually, again, with a big part of my heart left behind. A big part of my life away from me. And no matter what I do to stay connected, it’s not the same. I realized now, no matter what I choose, where I choose to live, I cannot have it all. And that I will always be giving up something. Oh the luxury to have it all…
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".