Gender and Age Patterns on WhatsApp Statuses as Used by Jordanians: A Sociolinguistic Perspective
Bibliographic record
Abstract
This study aims at investigating the WhatsApp statuses as used by Jordanian people from a sociolinguistic point of view. It attempts to examine the use of the WhatsApp statuses in relation to the impact of gender and age on the topic being used. To achieve this goal, 400 statuses were collected from Jordanian males and females who are divided into two main age groups: the first one consists of participants whose age is above 30 years old, and the second group whose participants are under 30 years old. Then, the data were analyzed quantitatively and categorized based on the main following topics; religious, social, political, economic and fixed statuses. The results show that gender and age have essential impacts on the statuses being used. For example, the religious statuses are the most frequently used topic by Jordanian females whereas the social statuses are the most frequently used topic by Jordanian males. However, the political and economic statuses are the least frequent statuses used among Jordanian. Moreover, the results show that the most frequently used topic among males who are above 30 years old is the fixed statuses suggested by the mobile itself whereas the most frequently used topic among males who are under 30 years old is the social topic. On the other hand, the impact of age among females is clearly manifested in the use of the fixed statuses suggested by the mobile itself. For instance, the females who are above 30 years old use the fixed statuses more dramatic than the females who are under 30 years old. Also, the fixed statuses are the second frequently used topic by the females who are above 30 years old whereas they are the third frequently used topic by females who are under 30 years old.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".