Bibliographic record
Abstract
After a critical review of the impact of change on people’s lives, we report on an empirical study highlighting three major aspects of academic life that the pandemic affected, providing supporting examples. The method used is first textual analysis for the critical review based on what the literature identifies as difficulties brought about by change (CBIA, 2022; Senge, 1990). The empirical study is of a qualitative nature (Creswell, & Poth, 2018) based on the analysis of observations in a personal journal, and aims at uncovering academic concerns during the pandemic. The findings will be valuable to academics to reshape their ‘new normal’. Results include for the theoretical part of the literature review the fact that change impacts people and one cannot come back to prior positioning. Several findings from the analysis of the observational notes are centered around three main areas. The first issue was due to the short time span for new implementations and hence no time for foresight. This encompasses consequences of trial and error, more administrative control, and uncertainty of outcomes with contradictory discourses been held. Added to that there was a human cost that far exceeded what would normally be the case. For instance academic colleagues quitting or retiring early, unevenness in support provided, isolation in some cases compared to overabundance of support in others, perhaps even favoritism. The third major observation pointed to consequences on the instructional context. In this case, a number of positive outcomes were noted. More effort was placed on student engagement and learning, and it was all made visible. More activities were devised based on gaming strategies, and serious work was made more motivating. A better feel for knowledge integration was possible due to on-line learning for students who put some effort into it. Some observations however led to drawing conflicting conclusions. Finally, we discuss new future pathways. For instance, it is important to develop self-regulation in students and resilience for all concerned. There also appears to be a need to provide active support to everyone on an on-going basis as we move past the crisis.
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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 | 0.041 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".