Bibliographic record
Abstract
Purpose The purpose of this paper is to explore how a small group of adolescents in an alternative care and treatment program develop digital literacy skills over time while immersed in a rich media setting. It also explores how the students use new media tools and affordances to “perform” their identities and to present themselves within their classroom community. Design/methodology/approach This ethnographic case study research involved seven students from a Canadian alternative school that provides educational programming for students from government approved care, treatment, custody and correctional facilities. Through an integrated arts-based curriculum, with a thematic focus on community and identity, the students used a variety to digital tools and media to create an “All About Me” book. Findings The students used inquiry-based learning and multiple modes of expression, facilitated by the multimodal, multimedia nature of digital media, including both screen-based and tangibles as essential components of knowing and communicating. The maker pedagogies employed in this intervention facilitated self-directed learning, as well as the development of perseverance and self-confidence. Originality/value In many work environments individuals are required to have knowledge of emerging technologies, and to employ this expertise in their work. Teaching students how to navigate their way through unfamiliar technology, to reflect on the process, and to communicate effectively, are important in both academics and future work environments. The authors continue to work with this group of students in the STEAM-3D Maker Lab and emphasize learning through discovery, design and the development of important skills such as perseverance, troubleshooting, resilience and collaboration.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".