How Our Technology Use Changed in 2020: Perspectives From Three Youths
Bibliographic record
Abstract
The Technology and Adolescent Mental Wellness program (TAM) is a research program with the primary goals of promoting research on the topic of adolescent technology use and mental wellness, creatively disseminating that research, and fostering community among stakeholders. Our foundational question is this: How can technology support adolescent mental wellness? Youth are key stakeholders in pursuit of this foundational question. In this commentary, we invited 3 members of TAM's youth advisory board to respond to the following question: "How did your technology use change in 2020?" Jessica, Jared, and Babayosimi describe their technology use during COVID-19 as dynamic, and neither uniformly positive nor negative. Further, these 3 youths differ in their perceptions of the same technologies-social media and online school, for example-as well as their perceived ability to self-regulate use of those technologies. We invite you to weigh these perspectives just as we do at TAM-not as empirical findings in themselves, but as examples of youth ideas for future empirical investigation.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".