Starting from ‘scratch’: Building young people’s digital skills through a coding club collaboration with rural public libraries
Bibliographic record
Abstract
While digital infrastructure is clearly a critical factor in addressing the digital divide for rural society, it is only one component in realising the benefits of information and communication technology (ICT). It is increasingly acknowledged that citizens, governments, and businesses need to develop skills and motivations to use technologies. It is also recognised that young people and their rural communities are among those who gain the least from opportunities to engage in and benefit from an ever-evolving digital society. As with other areas of rural development, local community institutions and actors assume their own leadership in developing initiatives to overcome challenges and advance digital literacy and in this regard, public libraries have led and continue to hold considerable potential to champion this area. This article reports on the experiences of a 14-month community-based collaborative research project with public libraries engaged in a process of developing coding clubs for children and youth in rural Manitoba, Canada. Our research sets out to answer the questions: first, whether it is viable for public libraries to cultivate advanced digital skills among rural youth and contribute to bridging the rural-urban digital divide by running coding clubs following the CoderDojo model? And second, what are the critical conditions to ensure the success of public library coding clubs? In examining some of the experiences encountered in adopting the coding club as a model of digital literacy building, we discuss wider themes for rural public libraries interested in advancing digital literacy building within their communities.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| 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".