The ethical and practical implications of systems architecture on identity in networked learning: a constructionist perspective
Bibliographic record
Abstract
Through relational dialogue, learners shape their identities by sharing information about the world and how they see themselves in it. As learners interact, they receive feedback from both the environment and other learners which, in turn, helps them assess and adjust their self-presentations. Although learners retain choice and personal agency, even the most neutral-seeming technological environment may encourage some ways of interacting whilst discouraging others. Taking a constructionist perspective, the authors first compare peer-to-peer interaction in online and face-to-face environments. Online self-presentation is adjusted using identity management tools. These tools may provide efficient ways to locate and interact with other learners as well as protection mechanisms for personal information. In particular, the authors discuss the effects of anonymity and pseudonymity on trust and social capital. To illustrate these concepts, the authors discuss two social networking systems, iHelp and The Landing, and how their underlying architectures may affect discourse and identity management. Throughout, there remains a tension between the individual self versus the self as part of a social group. The authors recommend careful consideration of the effects of systems architecture on both the individual and the community – thereby balancing the needs of the individual with her learning communities. From an ethical standpoint, only then can both individual and community flourish online.
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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 | 0.037 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.132 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".