Thinking geographically about how people become wiser: An analysis of the spatial dislocations and intercultural encounters of international migrants
Bibliographic record
Abstract
Our research seeks to answer whether immigrants see the act of relocating to a different country and the place-based intercultural encounters associated with this migration as being conducive to wisdom. The study is interested in qualitatively analysing the spatial constitution of wisdom and the perceptions of wisdom that immigrants possess. This situated approach looks at wisdom in relation to narrativity, subjectivity, and positionality, as opposed to the now-dominant psychological view of wisdom as a quantifiable phenomenon that can be measured on a positivist scale. Both inter-country migration and living amongst other ethnicities in migrant cities are spatial processes of relevance to our attempt to think geographically about how people become wiser. We investigate empirically and develop the foregoing themes by drawing on in-depth semi-structured interviews conducted with Romanian immigrants in Ontario, Canada, between 2014 and 2018.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".