“Homeless” at Home: Linguistic, Cultural, and Identity Hybridity and Third Space Positioning of Kenyan Urban Youth
Bibliographic record
Abstract
In Kenyan urban locations where speakers of a myriad of different languages and cultures converge, young people have experienced ambivalence, ambiguity, and contradictions regarding language, culture, and identity that they can ascribe to themselves, both as unifying factors at the national level, as well as marking their identity as urban youth. In an attempt to bridge the ethnic divide, and the divide between what they perceive to be traditional values and the urban, modernized values, Kenyan urban youth have developed a “hybrid” language called “Sheng”. This language has opened up avenues for renegotiating their identity and cultures, moving them beyond unitary, fixed identities and binaries of traditional versus urban, and local versus global. Thus, this paper uses the post-colonial notions of hybridity and the third space to interrogate ways in which these youth have challenged the established codes of their identities, and negotiated their ambivalences in a third, hybridized space that is fluid and shifting. Educational opportunities for Kenyan urban youth within the third hybridized space positioning are discussed. Au Kenya, dans les centres urbains, là où convergent une myriade de langues et de cultures, les jeunes ressentent des contradictions et une certaine ambigüité quant à leur langue, leur culture et leur identité, cette identité qui les caractérise au niveau national comme de jeunes urbains. Ces mêmes jeunes ont développé une langue « hybride » appelée “Sheng” dont le but est clairement de combler la division ethnique, ainsi que la division entre ce qu’ils perçoivent comme étant des valeurs traditionnelles et celles comme étant des valeurs urbaines ou même modernes. Cette langue a ouvert la possibilité de renégocier leurs identités et leurs cultures, et d’aller au-delà même de l’unité, c’est-àdire au-delà des identités fixes et des binaires, tels que traditionnel vs. urbain ou local vs. global. Cet article utilise donc les notions postcoloniales d’hybridité et de troisième espace afin d’analyser la façon dont ces jeunes ont défié les codes établis de leurs identités respectives et comment ils ont négocié leurs ambigüités dans un troisième espace hybride, fluide et variable. Cet article examine également les différentes possibilités éducatives pour les jeunes kenyans urbains dans ce troisième espace hybride.
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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 | 0.002 | 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 | 0.011 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".