Facebook’s “Free Basics”: For or against community development?
Bibliographic record
Abstract
A recent discussion on a prominent community informatics (CI) listserv revealed arguments for and against the Facebook’s Free Basics platform among researchers in the field. To continue and enrich the conversation, this study first examines the contrasting stances revealed in the CI listserv discussion and derives the CI researchers’ major concerns about the platform. Under the light of these concerns, we then explore the nature of Facebook’s Free Basics in relation to community development through analysis of one of the forefront services that Free Basics offers, i.e., Facebook. Specifically, we examine relationships between uses of Facebook and information technology (IT) identity formation and social capital. We argue that although projects operated by private companies may possess potential for supporting community development, much consideration is needed in embracing the technology solutions due to the risks and restrictions they can impose on its users. We also suggest the CI researchers to open the next round of discussion regarding ways to thoroughly assess possible flaws of Free Basics and help users of the platform make more informed decisions. IT identity is a new theory that can help shed new light on the challenges of using platforms such as Free Basics and their contribution to community 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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| 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".