EVERY CLICK YOU MAKE: ALGORITHMIC LITERACY AND THE DIGITAL LIVES OFYOUNG ADULTS
Bibliographic record
Abstract
Critical digital literacy comprises subsets of medium- and content-related skills necessary for digital privacy and digital citizenship. Frameworks for defining and evaluating digital literacy proliferate in academia and policymaking; however, in a networked climate subsumed by dataveillance, algorithmic bias, political bots, and deep fakes, these frameworks need to be updated. Algorithms may be the greatest determinant in sociopolitical online interactions and information gathering, and without a multivalent literacy of algorithms, nuanced understandings of digital privacy and digital citizenship may be unachievable. We therefore propose ‘algorithmic literacy’ become an essential element for digital literacy in young adult media education. Researchers have highlighted how intersectional aspects of gender, ability, and socioeconomic status are stronger predictors of low digital literacy than age. Following a tradition of participatory (rather than protectionist) research about youth privacy online, our research foregrounds young adults’ practices and perspectives on algorithmic culture in order to co-develop a framework for algorithmic literacy. Our paper shares findings from a participatory project co-designing an algorithmic literacy toolkit with young adults as co-researchers and participants. We created a curriculum focusing on reviewing the current critical scholarly literature, policy, and popular discourse on algorithms. After two weeks of intensive research, our student co-researchers met amongst themselves to devise a sustainable, ‘living-document’ type of toolkit, comprising a website, an Instagram page, and a Medium blog. Reflected in the toolkit's name, The Algorithmic You uses an intersectional lens to facilitate peer-oriented ‘self-discovery’ of how algorithms shape and produce interactions in the everyday lives of young adults.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".