DTJournal: Instagram Stories Metrics
Bibliographic record
Abstract
Instagram Stories is a tool that lets users post media material (images, photos, and videos) that vanishes after twenty-four hours but which can be saved to your account. Story`s analytics is shown only to the page owner: 1) interactions (replies, profile visits, and sticker taps) and 2) discovery (amount of accounts reached with this story). Example of analytics from last DTJournal`s story dedicated to a new Section is showed in Figure. Breathtaking growth history of Instagram: 1 million users within 2 months of being open (October 2010), 10 million users in one year, 100 million in 2013, and 1 billion users in 2018.3 With that overwhelming growth tendency (5% Instagram growth per quarter, 3.14% Facebook growth, and 2.13% Snapchat growth),3 we can predict that after next 8 years the total amount of its active users will reach 2 billion. Some journals use the advantages of Instagram and received a huge help in journal`s growth and attraction of new readers and authors. Among those publications are PRS Global Open (Instagram: @prsglobalopen has 2,433 followers), PRS (Instagram: @prsjournal – 12.3K followers), The New England Journal of Medicine (Instagram: @nejm – 184K followers), etc. Some updates to the Instagram Stories have recently been added, such as the ability to ask questions to the public, thereby increasing interaction with the journal’s audience. And the main question that every editor and publisher of newly launched or other existing journal should ask themselves is: “With more than 1 billion monthly active users (or potential customers), is our peer-reviewed journal on Instagram yet?”
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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 | 0.003 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.038 |
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".