Bibliographic record
Abstract
This research article undertakes a timely interrogation of the anti-woman digital practices on social media environments, particularly Twitter – specifically, practices seeped in gender bigotry and ageism. It introduces the relatively unexplored concept of digital temporality vis-à-vis digital feminist analysis, and relies, to a limited extent, on empirically examining Indian Twitter primarily and American and Australian Twitter to a lesser extent. The article analyses how image-aesthetics for women, dependent on their age (manifested in appearance and perception of women’s digital image), are a patriarchy-propped digital trend and how that promotes a culture of digital ageism, predicated on women’s appearance. This study also examines and establishes how women users on Twitter are susceptible to continual ageing as images on the digital screen and how digital ageing is a gendered phenomenon. The scope of this article is to examine digital ageism from theoretical perspectives, and offer empirical examples from news portals and Twitter media to substantiate some crucial arguments. This project, of course, also opens more scope to quantitatively account for digital ageism through detailed empirical studies as is also the scope to examine the various subversive tools that could potentially be utilized in gendered rubrics to defy or negate sexist ageism on social media.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".