Re-Understanding Intersectionality From a Dynamic Ecological Contextual Standpoint: Lived Experiences of Minoritized Asian Gay, Bisexual, Queer and Men-who-have-sex-with-Men (GBQMSM) Communities in Canada
Bibliographic record
Abstract
Intersectionality research emphasizes the importance of social context in understanding the experiences of marginalized communities. While Bronfenbrenner’s Social Ecological Model is often used, it overlooks the dynamism of interactions between systems over time, limiting its effectiveness for intersectionality studies. Chong, Goh, Lye, and Queer-lleagues introduce the Dynamic Ecological Context framework as part of the SPLICE framework in 2025, which considers these interactions more comprehensively. This paper applies the Dynamic Ecological Context to a case study investigating the Lived Experiences of Gay, Bisexual, Queer, and Men-who-have-sex-with-Men (GBQMSM) communities in Canada before and during the COVID-19 pandemic. I argue that when applying the model, intersectionality researchers should incorporate (i) ongoing interactions between systems, (ii) stakeholders’ autonomy, (iii) the existence of multiple physical and virtual ecologies, and (iv) how changing circumstances affect system interactions, influencing the empowerment or disempowerment of marginalized individuals.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.037 | 0.038 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".