Social Work Digital Storytelling Project: Digital Literacy, Digital Storytelling, and the Makerspace
Bibliographic record
Abstract
Purpose: The Social Work Digital Storytelling project was a research study undertaken to (1) enhance digital literacy of practitioners and students through digital storytelling training, (2) diversify engagement in a local public library technology hub (the “makerspace”), and (3) understand and enhance social work leadership knowledge among students and practitioners through the creation and sharing of leadership-focused digital stories. Method: Free hands-on digital storytelling workshops where social workers/students created stories about leadership exposed social workers to technologies accessible in the community and provided hands-on experience using hardware (e.g., IMac computers, digital cameras, portable data recorders, and a recording booth) and software (e.g., Adobe Photoshop, I-Movie, and GarageBand) as well as online social media platforms (e.g., Flickr, YouTube, and Facebook). Results: Before and after the workshops, participants completed a brief online qualitative self-evaluation survey through which they reflected on their skills, values, and beliefs about digital technology in practice. Participants gained knowledge of perspectives of online ethical tenants and exposure to Creative Commons Copyright and the NASW Technology Standards of Practice. Discussion: Prior to participation, the social workers reported fear and hesitancy using technology. After workshop completion, workers experienced a greater sense of confidence using digital technology as well as identifying organizational and systemic issues, which hindered field-based technological engagement.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".