Bibliographic record
Abstract
This article analyzes the diverse migratory experiences that inform the narratives of refugee women from Nepal, the Democratic Republic of Congo (DRC), and Iraq while these women navigate higher education as refugees in a small city in the U.S. It is important to contextualize that these women’s experiences take place in Lancaster, Pennsylvania, especially given Lancaster’s unique relationship to refugees. While refugee numbers have lagged more recently due to restrictions placed by the Trump administration, the longstanding commitment on the part of organizations like Church World Services and Bethany Christian Services to provide support to refugees signifies, to a certain degree, that Lancaster is different than the rest of the U.S. when it comes to welcoming refugees (Lancaster Online Staff Writer, 2019). To analyze our informants’ migratory experiences which resulted in their pursuit of higher education in Lancaster, Pennsylvania, the article explores informant participation in a wide range of meaning-making practices. In doing so, the article analyzes our informants’ varying levels of struggle with imposed narratives. These imposed narratives have to do with refugees as they resettle in the U.S. The perception of refugees as victimized, impoverished, and destitute informs some of these refugee women’s sense of being pitied in their new social structure. Grappling with these perceptions also challenges the informants’ ability to construct their own narratives. The powerful yet nuanced influence of imagery on social discourses is pivotal in terms of shaping the narratives of refugees. In turn, this imposed imagery and imposed narratives render authentic narratives all the more necessary.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.039 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 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".