Alternative Ways of Historical Knowledge Dissemination: Black History on Instagram
Bibliographic record
Abstract
Historical knowledge dissemination on social media platforms has become increasingly prevalent in the digital age. However, only few studies focus on the implications of learning historical knowledge from social media platforms, rather than focus on debatable areas of history. This research builds upon two studies in this area of research (Birkner & Donk; 2020, Liu; 2018). This research intervenes in this area of study by focusing on Black history on the social media platform Instagram. This research uncovers how Black history is framed on social media platforms, if and how Instagram users are learning from these Instagram posts, and why social media has become a prevalent tool for historical knowledge dissemination. This is done through a multimodal discourse analysis. This research provides a foundation for further inquiry into this area of study while also highlighting where further research in this area could go.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".