Bibliographic record
Abstract
Witnessing some of the most horrific scenes of terror, hate, and harassment, this work is about “evincing” 1 —evincing in and with the stories, voices, screams, hurt, and pain of the victims, the racially and culturally marginalized scholars who are harassed for their research. My reflections, while they led to a poetic art piece, are about the critical “ongoingness” of continuously revealing the evidence and truth of the unsafe world of doing, being, and sitting with online research. In the evincing, we censure and condemn hate and harassment while vituperating and execrating the perpetrators, cyberspaces, systems, and institutions that target, abuse, mistreat, and malign women; Black, Indigenous, and people of colour (BIPOC); queer; disabled; and other marginalized scholars. Evincing is the ways in which their research work and lives remain present and alive through this art and those to come. In this evincing, their experiences of racism, misogyny, violence, isolation, powerlessness, helplessness, frustration, and fear are not simply spectacles for awareness. Rather, they are the grounds from which our methodologies for witnessing these scenes become our vocabularies in resistance, solidarities, and solutions. Evincing, then, is how we mobilize against these encounters of hate and harassment in celebration of safe, brave, healthy, and continuous academic journeys for all scholars.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".