LANGUAGE VARIANCES IN DEFINING YOUNG WOMEN IN NORTHERN UGANDA HUMANITARIAN SETTINGS
Bibliographic record
Abstract
This paper, which focuses on formerly displaced communities in post-conflict northern Uganda, discusses the variance between the way formal institutions view young women’s identities and how young women see themselves. Based on a qualitative study that used in-depth interviews and focus group discussions, findings indicate that the infantilizing and victimizing language adopted by these institutions does not reflect the identities of young women in the post-conflict setting. These women argue that terminology such as “child mother” and “child soldier” is disempowering, denying them the prestige of adulthood yet disassociating them from childhood. The intersecting nature of their perceived identities hinders their access to humanitarian assistance targeted specifically to children or adults, since they are not recognized as clearly belonging to either group. The use of the term “child mother” effectively penalizes young women for engaging in adult (sexual) behaviour, while denying them the adult status that mothers are normally accorded. This article argues that sustainable post-conflict reconstruction, with efficient access to and use of humanitarian assistance, demands insitutional adoption of contextually inclusive language that recognizes young women’s professed identities and is reflective of local experiences and realities.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".