When gender, colonialism, and technology matter in a journalism startup
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.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".