A Sociolinguistic Study of Choosing Names for Newborn Children in Jordan
Bibliographic record
Abstract
<p>This study aims at the study of newborn names in Jordan of a sociolinguistic perspective. This study tries to detect the difference in naming newborns in Jordan over the decades - from the seventies to 2015 due to the result of some factors that may have affected the Jordanian society, whether historical, religious and/or social. The data necessary to complete the study was obtained from the Civil Status Department and the Department of Statistics. The data obtained consisted of names of both sexes during the time period from the seventies until the early year of 2015, a random sample of personal names within the same family were also provided. The data was analyzed quantitatively. The study revealed that there is a clear change in the choice of newborn names-male and female-in Jordan, whether a change in sounds or in morphemes. In specific, names during the seventies were strongly linked to the culture and the values, religious or social, in which the people believed in. During the eighties and nineties, names were associated with certain social values, however, some names were shown to be affected by urbanization or modernization. And with the beginning of 2000 up to 2015, peoples directions towards naming newborns changed due to the advent of globalization, associating with development and urbanization, and moreover, the influence of different cultures on the community.</p>
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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 | 0.003 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".