Digital Inclusion Through Algorithmic Knowledge: Curated Flows of Civic and Political Information on Instagram
Bibliographic record
Abstract
Social media platforms are a critical source of civic and political information. We examine the use of Instagram to acquire news as well as civic and political information using nationally representative survey data gathered in 2019 in the US, the UK, France, and Canada (n = 2,440). We investigate active curation practices (following news organizations, political candidates or parties, and nonprofit organizations or charities) and passive curation practices (liking friends’ political posts and those from parties or politicians and nonprofits or charities). Young adults (18 to 24 years) are far more likely to curate their Instagram feed than older adults in all four countries. We consider two possible explanations for this behavior: political interest and an understanding of how algorithms work. Young adults have more (self-assessed) knowledge of algorithms in all four countries. Algorithmic knowledge relates to curation practices, but there are some cross-national differences. Algorithmic knowledge is theoretically relevant for passive curation practices and the UK sample provides support for the stronger role of algorithmic knowledge in passive than active curation. In all four countries, political interest positively relates to active and passive curation practices. These findings challenge depictions of young adults as news avoiders; instead, they demonstrate that algorithmic knowledge can help curate the flow of information from news organizations as well as civic and political groups on Instagram. While algorithmic knowledge enables youth’s digital inclusion, for older adults, the lack of knowledge may contribute to digital exclusion as they do not know how to curate their information flows.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".