Bibliographic record
Abstract
As an emerging scholar committed to social justice and anti-oppressive praxis, I entered my master’s program in Geography at York University, Toronto, with the goal of contributing to new theoretical insights and meaningful outcomes for research participants in Thailand. While initially the concept of communityengaged research appeared to alleviate the tensions between these two goals, the realities of the university’s constraints on graduate student research coupled with those of the COVID-19 pandemic have made it clear that this endeavor would not be straightforward. The inherent messiness of balancing academic matters (e.g., contributing to new theory and demonstrating an adequate level of rigor) with social justice concerns (e.g., eliminating epistemological violence and contributing meaningful outcomes for research participants) in community-engaged research has only intensified as COVID-19 has reconfigured our social relations, exacerbating existing inequities and restricting our social mobility, particularly across international borders. In this reflection, I consider how remotely collaborating with local research assistants in my own graduate research project typifies these tensions. More specifically, I posit that the COVID-19 pandemic has further underscored the importance of researchers, particularly white men researchers such as myself, to be willing to consistently re-evaluate our projects, and embrace flexibility, accountability, and the removal of ego from our work.
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 | 0.170 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.068 | 0.081 |
| Scholarly communication | 0.032 | 0.018 |
| Open science | 0.010 | 0.039 |
| Research integrity | 0.018 | 0.048 |
| Insufficient payload (model declined to judge) | 0.007 | 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".