Implications of the Marginalisation of Social Sciences in the Fight against the Covid 19 Pandemic: A Humanities Perspective
Bibliographic record
Abstract
In the history of pandemics that plagued humanity, COVID-19 represents a catastrophic global health crisis. The pandemic has placed a huge burden on health care systems around the globe. Due to its easy transmission from one individual to the other, COVID-19 prevention require large scale behaviour change. Through the recommendations of the WHO, governments across the world have enacted policies of social distancing, national lockdown, wearing face mask, release of inmates from prisons, temporary citizenship to migrants and refugees. In fostering the contingent measures to manage the pandemic between March and December 2020, most governments have consulted epidemiologists, public health experts, virologists among other pure sciences disciplines. However, notably absent, or poorly represented were the insights from social and behavioural scientists. The researchers argue that the absence or marginalisation of social sciences in the battle against the pandemic creates a myriad of gaps among the mechanisms crafted to manage the pandemic. The aim of this paper is to provide the entry points of social scientists in the fight against the pandemic. Through the use of insights of sociology and social work disciplines, the researchers noted that social scientists are involved in behaviour modification, compacting fear and anxiety, promotion of human rights, psychosocial support to vulnerable populations; and understanding the pandemic in the scope of globalisation. In terms of recommendations, we suggest that social workers and sociologists need to depend on the repertoire of their disciplines in order to effect change in different communities during the pandemic and its aftermath.
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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 | 0.038 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.021 | 0.122 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 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".