Crossing the Bridge of the Digital Divide
Bibliographic record
Abstract
Crossing the Bridge of the Digital Divide: A Walk with Global Leaders explores the combined effect of the rapid growth of information as an increasingly fragmented information base, a large component of which is available only to people with money and/or acceptable institutional affiliations. In the recent past, the outcome of these challenges has been characterized as the 'digital divide' between the information 'haves' and 'have nots' along racial and socio economic lines that seem to widen as time passes. To address the issues of digital equity and digital inequality in an effort to bridge the digital divide, educational scholars, researchers and practitioners are in positions to ensure equitable opportunities are made available for people of all ages, races, ability, sexual orientation, and ethnicity in support of social justice for bridging the digital divide. The digital divide addresses issues concerning equal opportunity, equity and access that have an effect on the development of marginalized and otherwise disenfranchised populations within and across systems nationally and internationally.The contributing authors- representing Unites States, Canada, South Africa, New Zealand, and the UK - posit that education institutions can serve as the bridge to close the digital divide for students who do not have access to information technology in their homes. At a time when more computers are made available in schools than ever before, the digital divide continues to widen and fewer people in the lowest SES groups are given the opportunity to join the world of computer technology and the internet. As a result, the influence of leadership activity on institutional racism, gender discrimination, inequality of opportunity, inequity of educational processes, digital exclusion, and justice have gained currency and attention.The contributing national and international authors examine the digital divide in terms of social justice leadership, equity and access. It is within this context that the authors offer discussions from a lens of their choice, i.e. conceptual, review of literature, epistemological, etc. By adopting an educational approach to bridging the digital divide, researchers and practitioners can connect and extend long established lines of conceptual and empirical inquiry aimed at improving organizational practices and thereby gain insights that might be otherwise overlooked, or assumed. This holds great promise for generating, refining, and testing theories of leadership for equity and access, and helps strengthen already vibrant lines of inquiry on social justice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".