ICTs as placed resources in a rural Kenyan secondary school journalism club
Bibliographic record
Abstract
In this study, we draw on three interrelated concepts, i.e. placed resources, multiliteracies and the carnivalesque, to understand how information and communication technology (ICT) resources are taken up within the context of a print-based journalism club. Our research participants attend an under-resourced girls’ residential secondary school in rural Kenya. We used ethnographic methods to document how the 32 club members (aged 14–18 years) used digital cameras, voice recorders and laptops with connectivity to research, conduct interviews, photograph and create texts. Key findings include shifts in identity performance, journalistic competence, and hierarchical distinctions and societal power; growing writer activism and audiences; and the emergence of imagined identities and transformative social futures. Our research challenges current skills-based approaches to introducing new literacies and highlights how the introduction of new ICT resources, when situated within collaborative practices (both research and pedagogical), can result in enhanced literacy learning and text production. These changes have not been without tensions and dilemmas, including the extent to which such practices could only occur outside the formalized classroom with its traditional practices, structures and emphasis on exam results. In addition, some of these tensions raise new questions about the role of ICTs as pedagogical tools and the tendency to ‘romanticize’ their potential.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".