Sieves and reeds: Identity, cohesion, and be-longing in a glocalizing space. Young Oromos in Toronto [Ontario]
Bibliographic record
Abstract
This is a qualitative study examining complex micro and macro social processes of negotiating drastic shifts in identity, cohesion, and belonging in the context of refugee displacement. A community-based small sample of 18 self-identified research participants is selected from among young Oromos living in Toronto. While most were born in Ethiopia, some were born in Somalia but all came to Canada as refugees or as children reuniting with families who fled Ethiopia as refugees. Innovative methodology was designed to capture the fluidity and complexity of micro processes as they interplay with macro processes in the wider society. Rich qualitative data were generated through loosely structured conversations, telling stories, singing songs and chanting poems, as well as evocative exercises, group reflections, and debates. Participants met every week in four one-to-one and three small group weekly sessions for an average of seven weeks in two rounds. This process built up to a larger focus group debate at the end of the second round of weekly sessions. Six events of ethnographic activity participation were completed intermittently. Data were analyzed using critically interpretive approaches and strategies of critical reflexivity. Concepts and theories from across multiple disciplinary and ontological boundaries were critically engaged and an empirically grounded conceptual framework, dispersal-affinity, was developed through constant goings back and forth between the data and the literature. Dispersal signifies globalizing movements of expulsion and dispersion for refugees while affinity signifies localizing movements of yearning to relocate disrupted social ties. Dispersal-affinity depicts a constant dialogue between the past and the future, the global and the local, the conscious and the unconscious, as well as the individual and the collective. Findings indicate that identity and cohesion are simultaneous processes neither separable from nor reducible to each other and that these processes produce a sense of be-longing simultaneously. Furthermore, they indicate that these processes are directional movements where participants long away from spaces of perceived violence and oppression towards spaces of perceived healing and liberation. The implications of these findings for social work and social justice are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".