Bibliographic record
Abstract
Traditional legacy media have found themselves between a rock and a hard place —struggling to find their foothold with younger audiences and at the same time, forced to adapt to the demands of the industry minimizing the presence of local journalism. While social media has proven itself to be a means of connecting journalists to audiences, it is still unclear who is truly seeking out these journalists. The dominant perspectives on how social media could help address the challenges journalism faces today have been primarily preoccupied with how social media is used by traditional legacy media to connect with audiences. This study instead turns to social media environments where the people are seeking to connect to their local news through a well-known social media page in Calgary, yyc.clowns (now known as yycwave). The Instagram page has sustained a large following since 2019 and experimented with several types of content but has consistently remained a source of news for its large following, providing coverage and reporting on major stories of interest within Calgary. Through a discourse analysis of six news posts made by the account, this study seeks to answer how yyc.clowns defines the practices of local journalism, situating it as a viable source of news for its followers. This project consists of a video essay exploring the findings alongside a written literature review and discussion. The Instagram page acts as a liaison between its audience and legacy media, borrowing directly from legacy media news sources to deliver timely coverage of local topics of interest. This opens up the horizon for understanding how these popular social media accounts act as intermediaries allowing audiences that may not engage with legacy media to still receive news of relatively high quality.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.017 | 0.031 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".