Bibliographic record
Abstract
Several books examining various facets of gender and the Internet have been published recently. These various works encapsulate the different trajectories feminist research on and about the Internet have taken in the past decade. Early research on gender and the Internet focused on the interpersonal dimensions of CMC (computer-mediated communication), with a tendency to utilize discourse analysis in order to unpack the power relationships between men and women interacting online. Identity—how it was transformed or explored via the appropriation or construction of different genders—was also a key motif in early research. Cyberculture, as manifested in popular culture (films, books, television) was another focus, with feminists analyzing the masculinity of so much of this work, with its emphasis on “console cowboys” hacking the “electronic frontier.” More recent work has looked at the tension between feminist uses of the Internet versus the feminization of the Internet, as corporate interests actively staked out the women audience, creating commerce-oriented content. Another trend in feminist research has been to look at the everyday uses women are making of the Internet—their interactions with their family and work, and their content creation. Yet another tendency has been to look at the actual design of network communities to see how they have been gendered, and how technical design has influenced social interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".