Bibliographic record
Abstract
This paper investigates the intersections of race, identity, feminism, and digital media through a reflexive blogging project on Tumblr. The blog, Unsettledscholar explores decolonial thought, white racial socialization (Frey et al. 2022), and algorithmic biases (Philips and Ng-A-Fook 2024), that mediate the digital representation of marginalized voices. Using Jennifer A. Moon’s reflective practice methodology, the project examines how whiteness and settler privilege shape perceptions of race and identity in digital contexts, particularly in relation to Indigenous sovereignty and feminist praxis, Tumblr’s affordances as a multimodal, dialogic space (Bourdaa 2018), that facilitate a non-linear reflection on how colonial and patriarchal structures are perpetuated or challenged in digital environments. By engaging with multimedia content—such as images, text, and GIFs—the blog connects theoretical frameworks like Eve Tuck and K. Wayne Yang’s critique of decolonization as a metaphor and intersectional feminist scholarship with lived experiences of digital activism and advocacy. The project highlights how algorithms, rooted in colonial and capitalist systems, invisibilize Indigenous and BIPOC voices while amplifying certain narratives that align with settler colonial ideologies. It also considers how feminist counterpublics (Hoch et al. 2020) on Tumblr foster solidarity and resistance, creating spaces for nuanced discussions of relationality, care, and identity affirmation.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".