Displacement of the Scholar? Participatory Action Research Under COVID-19
Bibliographic record
Abstract
The impact of COVID-19 on conducting research is far-reaching, especially for those scholars working for or alongside communities. As the pandemic continues to create and exacerbate many of the issues that communities at the margins faced pre-pandemic, such as health disparities and access to resources, it also creates particular difficulties in collaborative, co-developed participatory research and scholar-activism. These forms of community engagement require the commitment of researchers to look beyond the purview of the racialized capitalist and neoliberal structures and institutions that tend to limit the scope of our research and engagement. Both the presence of the researcher within the community as well as deep community trust in the researcher is required in order to identify and prioritize local, often counter-hegemonic forms of knowledge production, resources, and support networks. The pandemic and similar conditions of crises has likely limited opportunities for building long-term, productive relationships of mutual trust and reciprocity needed for PAR while communities refocus on meeting basic needs. The pandemic has now not only exacerbated existing disparities and made the need for engaged, critical and co-creative partnerships even greater, it has also abruptly halted opportunities for partnerships to occur, and further constrained funds to support communities partnering with researchers. In this paper we highlight accomplishments and discuss the many challenges that arise as participatory action researchers are displaced from the field and classroom, such as funding obstacles and working remotely. An analysis of experiences of the displacement of the scholar exposes the conflicts of conducting PAR during crises within a state of academic capitalism. These experiences are drawn from our work conducting PAR during COVID-19 around the globe, both in urban and rural settings, and during different stages of engagement. From these findings the case is made for mutual learning from peer-experiences and institutional support for PAR. As future crises are expected, increased digital resources and infrastructure, academic flexibility and greater consideration of PAR, increased funding for PAR, and dedicated institutional support programs for PAR are needed.
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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 | 0.131 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.105 |
| Scholarly communication | 0.031 | 0.023 |
| Open science | 0.007 | 0.053 |
| Research integrity | 0.009 | 0.017 |
| 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".