Conversations from around the coffee table: Exploring subaltern leadership
Bibliographic record
Abstract
Moving towards social justice requires a deconstruction of the current work and leadership systems that contribute to and are rooted in oppression. (Re) visioning leadership must exist outside to dismantle the dominant discourses. In the community, social justice work has the opportunity to use love and hope to guide the processes. This article presents the findings from our, the coauthor’s convivio – we are a group of women living in Canada, members of the Central American and South American diaspora. We gathered around a coffee table to discuss how leadership currently operates and the possibilities for a more collective future. What we term “subaltern leadership” represents the how we navigate our positions as leaders amidst marginalization as newcomers and as women. What evolved in this dialogue was the question of “can the subaltern lead in the current structure and nature of work?”. The findings support the notion that representation is only the beginning and can mirror tokenism when the same structures remain. To truly support subaltern leadership, a more radical shift must occur.
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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.030 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".