Bibliographic record
Abstract
Abstract The migrant experience is above all a humbling one. There is often a wrong perception amongst those who live in places where migrants tend to go that the resilience we carry is due to some negative experiences we had where we came from; and that this justifies the hardships we endure in the places we choose to call home. The story is frequently very different. We move full of hopes and dreams, and many would not take the leap if conscious of all the barriers we are sure to face. The stories of migration are as diverse as the number of stars in the sky. And for that reason, the idea of migrants as a single group goes against any kind of reality if examined for more than a second. Although in Canada there are established categories of immigrants, such as economic migrants and refugees (and several others), within these categories are human journeys that are specific to each individual. Even inside a family we find different reasons for taking the leap. While one can choose to move away to have better professional opportunities, others might only be looking to get away from the mess their families are tangled in. To distance oneself from one’s birthplace can encompass the understanding that one’s comfort zone lies in a distant place, far away from social pressures – a chance to reinvent yourself as an individual.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.007 |
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".