Bibliographic record
Abstract
Forces of globalisation and economic competition enhanced by, among others, the digital technologies, are radically transforming the social context. Digital technologies are characterised by a powerful and pervasive Internet as well as the related information and communication technologies. Globalisation is facilitated by the universally accessible, reliable and inexpensive communication assisted by these digital technologies. However, there is growing and valid scepticism regarding the digitally influenced socio-economic emancipation. This scepticism is mainly driven by a lack of understanding of digital literacy as a holistic process of creating the necessary social, economic and political changes within a given context. The understanding of digital literacy therefore needs to join a number of seemingly divergent views of digital technology when dealing with these technologies’ benefits in socio-economic emancipation. This understanding of digital literacy should therefore be shaped and focused more on understanding how digital literacy impacts the poor and marginalised, especially in looking at the socio-economic welfare of these marginalised sections of the society. This article discusses digital literacy by firstly looking at the shortcomings of the available definitions and approaches and then recommends a socio-economic development-orientated definition. The article brings to the fore the most critical digital literacy issues for socio-economic development. These issues are important; they ensure that digital literacy is not viewed in isolation, but rather in terms of its outcomes and consequences, especially with regard to socio-economic development.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.008 | 0.051 |
| Scholarly communication | 0.027 | 0.038 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".