When gender, colonialism, and technology matter in a journalism startup
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article is based on an ethnographic study of a women-led journalism startup, identified as a digital and data innovator in North America. Studies of journalism startups have generally focused on growth in the startup space and claims to technological innovation, finding a persistence of traditional norms and practices. Feminist media scholars have not tended to engage in this area of study, focusing more on newsroom sociology and media representations, despite a long history of feminist Science and Technology Studies critique of other technical professions such as engineering and computer science. This study adds to our understanding of journalism startups by situating this ethnography within feminist, postcolonial, and Science and Technology Studies approaches. Our findings suggest the persistence of professional, industry, and economic constraints mapped on to gender, gendered understandings of innovation, and technology in journalism – as well as possibilities to transform them. We argue that gender and colonialism matter in this startup in expected and unexpected ways, from understanding the enduring nature of unexamined power relations within journalism to contributing to re-articulations of important questions of epistemology, method, and moral stance in digital journalism.
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.
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 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it