Blended Identities: Identity Work, Equity and Marginalization in Blended Learning
Bibliographic record
Abstract
This article is a theoretical study of the self-presentation strategies employed by higher education students online; it examines student identity work via profile information and avatars in a blended learning environment delivered through social networking sites and virtual worlds. It argues that students are faced with difficult choices when having to present an online self to a group that has physical contact with each other. A virtual self that is accurate, ideal or drastically different from the offline self will be met with various methods of exclusion or marginalization according to dominant offline norms and expectations as well as online notions of strangeness and inauthenticity. This study demonstrates that in blended learning the issues students face with offline marginalization according to how identity is performed and read are, at best, equally represented online with an uncritical self-representation and, at worst, compounded online when critical self-representation is met with resistance.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".