Bibliographic record
Abstract
The COVID-19 pandemic has prompted continuing constraints on the ability of students to interact with teachers and peers. Regarding this imposed segregation, what has not been considered is the effect of learners seeing self as other. With respect to augmentations of their body in interpersonal space by, (1) extending the body through witnessing themselves regularly in videoconferencing learning sessions, (2) isolating the body as a result of spending time apart from peers, social distancing at home, and (3) protecting the body through required mask-wearing where learners now consider who they represent in a mask, there are three important ways in which learners have felt unable to recognize themselves as they did pre-COVID-19. This migration from self to other, involving ingroup/outgroup distinctions, will be investigated from a number of perspectives—both sociological and psychological. Why the turning of self into other is problematic to the psyche will be discussed, as will the possible consequences for this ongoing lack of learner recognition long term, including focus on the new norms or embracing self-directed learning. Based on this analysis, the type of mentorship by teachers and parents that may be appropriate for helping learners contend with these changes will be recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".