Bibliographic record
Abstract
Journalism is an ever-growing industry. In the digital age of the 21st century, social media platforms like Facebook and Instagram (Meta) have become a place for engaging with audiences in an attempt to humanize news publications. Until the summer of 2023, social media platforms were open spaces for the accessible sharing of news. The reliance of journal publications on social media to interact with the audience not only built a boundary between personal and professional image for journalists but also made journalists’ interactions with their audiences very passive. With the changes implemented by Meta in response to Bill C-18, which took away news organizations’ accounts from their social media platforms in Canada, the process of engaging with audiences and presenting news changed to become more journalist-oriented. Furthermore, journalists participated more actively as they had to present themselves by using their personal accounts. Now that the line between professional and personal identity on social media is blurred, it provides space for discourse on how a journalist brands themselves on a platform that is silencing their work. My study investigates a local independent student news publication’s editorial board practices to observe how student journalists are resisting the news-sharing ban Meta has introduced. I found that the platform’s policy changes were mostly superficial, and loopholes were easy to find and exploit. The findings were used to make an Instagram account in the form of a guide for student journalists, documenting the process of bypassing the content-sharing ban.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.185 | 0.137 |
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".