Bibliographic record
Abstract
The COVID-19 pandemic has globally brought significant transformation in educational activities worldwide.1 Pakistan, although suffered comparatively less than rest of the neighboring countries was and still is, one of the affected regions. In March 2020, with strict restrictions on social gatherings, the conventional education methods were suspended temporarily with an urgent need to shift to online teaching methods.2 Online education is electronically supported learning that relies on the Internet for teacher/learner interaction and the distribution of learning resources. It may include audiotaped lectures, videos, text,animations, virtual training environments, real-time online lectures and interactive sessions with teachers.3 Online learning methodology was adopted by the west, long before it was even introduced in Pakistan. Considering, that the first-ever completely online course was offered in 1984 by the University of Toronto,4 it seems that we are lagging far behind. The West, with its already existing online learning technology, had the economic strength to survive the pandemic, yet such online practice and technological advancement was not available to the developing countries like Pakistan. The shift from traditional classroom teaching to online teaching was a huge change to adapt to for all stakeholders including institutional administration, faculty, students, and parents. High-quality online teaching is not only difficult to execute, but it is more demanding than traditional on campus teaching. It requires more upfront planning and groundwork and more individualized response and assistance for learner and teacher for which we were not ready. 3Despite strenuous efforts there were many shortcomings and blunders on part of management, teachers and students due to poor technical skills, reluctance, time constraints, inadequate infrastructure and absence of institutional strategies and support. Just like any other educational institute of this region where online readiness was non-existent, medical colleges were no different in the face of this challenge. This urgent requirement to ‘move online’5 added to the stress and workload of university faculty and staff who were already struggling to balance existing teaching, research, and administrative duties, not to mention the stress related to their own health and safety concerns during pandemic. Power outages and connectivity issues at both learner and faculty end were also one of the recurring problems
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".