Language, Identity, and ‘Development’: Intergenerational Schooling Experiences of Maasai Women in Tanzania
Bibliographic record
Abstract
Schooling has a contested, and frequently colonial, legacy. This study examines the impact of schooling across four generations of Maasai women in Northern Tanzania. Employing life histories methodology, this study explores how women’s experiences of schooling have impacted their language practices, their social, cultural, and economic capital (Bourdieu, 1991), and their imagined identities (Norton, 2013). It interrogates ways in which these women’s experiences reflect Tanzania’s changing socio-political and economic realities, through the eras of colonialism, independence, Ujamaa (Tanzanian socialism), and neoliberalism. Employing Darvin and Norton’s (2015) model of investment, this study examines ways in which ideology, identity, and capital intersect to shape these women’s investment in schooling and language learning, for themselves, their children, and their grandchildren. The findings suggest that women’s increased access to schooling across generations has resulted in nominal economic/class advantages, while also being closely correlated with the erosion of Maa language ability and rapid cultural change. Collectively, and in sometimes contentious dialogue with each other, these women’s narratives unsettle dominant notions of ‘development’, problematize mainstream projects of nation building and neoliberal modernization, and offer imaginations for alternative futures.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".