Breaking Down Barriers of Culture and Geography? Caring-at-a-Distance through Web 2.0
Bibliographic record
Abstract
Abstract This article analyzes Join My Village (JMV), an NGO-corporation partnership that aims to “break down barriers of culture and geography” using the “power of online communities.” JMV uses Web 2.0 technologies to entice online users in the USA to engage with content about women’s lives in Malawi. Each time a user clicks on JMV content, the corporate partners donate money to the NGO. Using discourse analysis and interviews, I examine how JMV encourages users to care about distant others and with what effects. I draw attention to the use of Web 2.0 in the campaign in terms of how distant others become entangled in social media users’ everyday lives and the types of engagement JMV encourages. I conclude that while JMV offers some possibilities for caring-at-a-distance, the contradictory messaging and the corporate aspects of the campaign need more critical analysis.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".