Bibliographic record
Abstract
T alking about race isn’t something many people feel comfortable or safe to engage in for an array of reasons, especially when the dialogue takes place publicly or if they are part of diverse nations like the United States, Canada, and others. Frankly, it is hard for me to do so as well because I am talking about my own personal story, not about racial phenomena observed in the society; I have a sense of unease from sharing my personal journey with racial issues mainly because of the uncertain emotional responses my story will evoke from others, particularly those about whom I deeply care and for whom I feel respect including colleagues, students, and friends. Nonetheless, I still choose to take the risk and candidly share my journey from the beginning point of lacking a racial identity to the current point of teaching multicultural education to future teachers in a predominantly white university. I decided to share my story for the following reasons: First, I clearly see the need for people to open up and engage in dialogue around one of the most avoided topics of all time, race and its attendant issues, because the more we avoid, the further we lag behind in building social justice for everybody. Second, by sharing my private, untold stories with others, I am inviting them to do the same or at least listen to different perspectives diverse individuals bring to the discourse of race and social justice. Finally, I would like to reflect on my personal journey so far in relation to racial issues and social justice and think about the journey ahead of me. I will start with how I became conscious about my racial identity and issues related to race.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.037 | 0.017 |
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".