Forgetting What We Have Learnt: The Digitalized Other and Implications for Students in COVID 19 Classrooms
Bibliographic record
Abstract
In a new world of nascent rules, restrictions, and lockdowns, a student’s biggest opportunity to connect with other similar individuals beyond their immediate circles is the digital classroom. Even with equipped tools of connection, students under COVID classrooms are ironically feeling the effects of disconnection and face risks for health concerns. As digital classrooms are shown to be prosaic, platformed, and productized, we will come to understand how building relationships with others but more so, of the self, is hugely hindered by faulty methods that do not work under new circumstances, and produce digitalized others which are consequential. It is as much an individual concern of a student’s performance as a statement on the public issue of current digital education. Sociological educators are essential in reshaping these pedagogical practices and beliefs, which can otherwise damage both students and their instructors.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.027 | 0.032 |
| Scholarly communication | 0.033 | 0.019 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".