Bibliographic record
Abstract
Toronto has been a centre for media covering international, Canadian as well as local news, but often important stories were left out-voices of people who have made the metropolitan area multicultural and vibrant.The challenges they faced in the increasingly divided city-between the haves and the have-nots-were largely ignored.It was a recognition of this gap that brought together a group of recent journalism graduates in the fall of 2021 to establish a journalistic startup: The Hoser with a "focus on local GTA [Greater Toronto Area] news with a progressive approach."The question this article will explore is the extent to which technologies enabled the emergence of The Hoser and the weight of other considerations, including an interest in pursuing a more participatory and more democratic form of journalism than that offered by mainstream media organizations.Bruno Latour's Actor Network Theory (ANT) offers an approach that enables journalism researchers to trace the diversity of actors-human and nonhuman-and actants, and the ways in which they come together to practice the production and circulation of stories.Some scholars have employed ANT to explore how digitization has impacted newsrooms.Others have examined "big data'' and journalism.Interestingly, there are few studies adapting ANT to better understand the emergence and practices of online journalism outlets.This gap focuses the research on The Hoser, exploring insights ANT may provide.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.025 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".