Rajieen: An Interdisciplinary Engagement Design to Revive and Sustain the Sense of Belonging
Bibliographic record
Abstract
“راجعين” or “Rajieen” is an interdisciplinary engagement design thesis project focused on preserving Palestinian cultural heritage. Originating from my personal experience as a Jordanian/Palestinian immigrant in Toronto, the project addresses the gap between diasporic communities and their indigenous culture and language. Through primary methods such as interviews, workshops, and surveys, combined with secondary research, Rajieen explores the experiences of Palestinian immigrants and their descendants. Drawing on theories of belonging and design methodologies, including co-creation, symbolism, and narrative space, the project culminates in an innovative board game at the heart of an interactive exhibition space that narrates the story of the project. The interdisciplinary approach not only aims to revive and sustain Palestinian cultural heritage but also to foster a deep sense of cultural belonging among immigrant communities. It is a reminder to celebrate the rich culture and profound land of Palestine, which no matter what it goes through, will forever be so fertile and continue to give, inspire, and share its rich culture, stories, and positivity to its people and all the people around the 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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".