Bibliographic record
Abstract
The major research questions of this paper and project are: how can Instagram be a better space for young girls? Which accounts and hashtags on Instagram can create an outlet for girls? On Instagram, some women edit their photos where they take in their waistlines and erase their acne; others use beauty filters where their faces are automatically changed to appear more beautiful (Tiggemann, Hayden, Brown, & Veldhuis, 2018). Teen girls aged 14-17 are using Instagram and it is evident that Instagram impacts their mental health. It is also apparent that Instagram can be a great outlet for girls (Li, Chang, Chua & Loh, 2018). However, there is a lack of resource materials for teen girls surrounding this topic (Li, Chang, Chua & Loh, 2018). Based on this reasoning, an infographic tool about how Instagram can be a better space for teen girls accompanies this paper. This paper and infographic will hopefully evoke conversations among girls. I believe that girls should be aware of different hashtags and accounts that are designed to spread positivity to enhance their experiences on Instagram (Li, Chang, Chua & Loh, 2018).
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.016 |
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".