Bibliographic record
Abstract
Women’s underrepresentation in high-tech roles derives from the historical, political and economic forces embedded in communication technologies. The advancement of Artificial Intelligence risks deepening this gender gap by replicating and displacing feminized labour. This paper employs a feminist, Marxist, political economy framework to examine the historical and contemporary marginalization of women’s labour in communication technologies. Henceforth, this paper asks, ‘How has the historical feminization of women’s labour in communication technologies shaped the structural gender gap in today’s digital economy?”. Tracing the feminization of women’s labour from the telegraph, typewriter and telephone to modern technologies like the computer, social media and AI, this study exposes the patriarchal social dimensions of communication commodities that maximize profit through feminized labour while isolating women to subordinate, ‘soft’ roles. Findings emphasize that historically, women’s affective labour was deemed economically suitable to operate communication technologies due to traits stereotypically associated with femininity, through attentive, emotional and submissive behaviour. Although female labour was foundational to technological advancement, this work was precarious. Simultaneously, the female gender became naturally attached to the identity associated with communication technologies. In today’s digital economy, women retain soft roles as social media labourers. Artificial Intelligence, exemplified by female digital voice assistants, mechanizes this feminized labour. Traditional gender norms have been embedded in digital systems, where AI automates the affective labour historically performed by women, intensifying their displacement to precarious gig work and reinforcing historical inequities. By illuminating the social forces shaping communication technologies, this paper argues that the persistent gender gap in the digital economy is rooted in the historical feminization of labour. As AI automates affective labour, it is apparent that communication technologies are primed to continue excluding women from an industry ripe with power and profit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".