Patterns of Identity Loss in Trans-Cultural Contact Situations Between Bantu and Khoesan Groups in Western Botswana
Bibliographic record
Abstract
According to Lamy (1979) and Pool (1979), ethnic identity comprises four distinctive features, namely linguistic identity, cultural identity, autonymic identity and ethnonymic identity. When an ethnic group is losing its identity because of pressure or attraction from a major or dominant ethnic group in a marked bilingualism situation (Batibo, 1992, 2005), the loss is usually progressive, starting from linguistic identity and ending with ethnonymic identity. Although this pattern has been attested in a number of cases, particularly in trans-cultural situations, there have been several exceptions. This paper is based on a study which investigated the patterns of ethnic identity loss in western Botswana, Southern Africa, which is both linguistically and culturally complex, due to the co-existence of Bantu and Khoesan groups. The study showed that the ethnic identity loss model can be distorted, where there are factors that have strong impact on people’s lives in terms of fundamental human needs. Also, strong external socio-political pressure, such as restrictions and group domination may contribute to this situation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".