Gender, Power and Digital Spaces: Change or a Different Shade
Bibliographic record
Abstract
Aim of the Study: The study aims to determine the existence of gender power relations in digital spaces and the possibility of change. Methodology: The information from the families was collected using a survey research method. A 1200 sample using the probability sampling technique from one Union Council of Rawalpindi was selected. Every UC has a 25000-30000 adult population, and based on that criterion; 300 families were chosen through a systematic random technique. Findings: The results showed that 81% of people have access to smartphones and 66% of people own them, establishing the first level of the emergence of digital spaces. The second level involves time spent on smartphones and their use to meet needs, which include basic, social, recreational, and financial needs. Gender differences in financial needs were found to be very significant. Significant gender differences were found in the recreational apps, and 63% of males as compared to 57% of females use these apps. However, there was no gender difference in the use of social media; 38% of men and 39% of women used these apps, respectively. At the third level, the presence of power in digital spaces was investigated through feelings of insecurity and abuse. Conclusion: The study concluded that digital technologies have broken the binary context of public and domestic spaces. The division of labor between men and women in digital spaces is gendered and linked to the capitalist economy. The study proved that these digital spaces have new shades of patriarchy, despite no change in gender power relations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".