Bibliographic record
Abstract
Social media apps like YouTube and Instagram came as platforms that allowed users to express themselves freely to their friends and families, but corporations changed social media down to its core. Due to the rising popularity of short video-based content on TikTok, platforms like Instagram introduced similar content to capitalize on the hype that TikTok created. In doing so, Instagram made changes to the content promotion algorithm to promote “Reels” over the other content options. Driven by profits the company stopped caring about their users, leading to backlash from the community. Creators on the platform started playing a visibility game (Cotter, 2019) to grow and be seen in user feeds, the “game” pushes them to make content they would not be making in the first place and following trends. In this paper I am looking at the case of a creator in the photography community affected by these changes in algorithm and analyzing the situation through a critical media theory framework. The study discusses the practices of the platform and the effects on the creator community while also looking at resistance from users. I also discuss a new potential alternative platform to Instagram for photographers, that markets itself as a platform built without an algorithm, for a community.
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 | 0.014 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".