Bibliographic record
Abstract
Throughout this paper, I analyze the deterministic aspects of Instagram and how those aspects affect agency. The meaning of Instagram is dependent on the creators of the app themselves. By placing Instagram on a spectrum, on the one hand, the reader can see that this app is a tool that can stratify the human need for social communication; on the other hand, it can see how its deterministic abilities affect both our mental and physical health. This shows through the relationships users build through the screen which are in-genuine relationships, ones that can lead to a loss of individual agency and freedom. The deterministic aspects of Instagram are further reinforced through the idea of techno-social engineering where it can be shown how social media applications can change the behaviour and feelings of their users simply through the posts they are exposed to. Lastly, the device paradigm in relation to Instagram as a deterministic tool showcases how the backgrounds and contexts of devices are becoming increasingly concealed and separated from our daily life. This results in a deterioration of genuine interactions within the physical environment and further reinforces the existence of the app that is constructed based on the creators and what they would like to accomplices. As a result, Instagram is a deterministic tool that is detrimental to our individual agency.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.083 | 0.007 |
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".